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Madhiarasan, M., Mohamed Louzazni, and Partha Pratim Roy. "Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting." International Journal of Photoenergy 2021 (October 27, 2021): 1–24. http://dx.doi.org/10.1155/2021/7238293.
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To forecast solar irradiance with higher accuracy and generalization capability is challenging in the photovoltaic (PV) energy system. Meteorological parameters are highly influential in solar irradiance, leading to intermittent and randomicity. Forecasting using a single neural network model does not have sufficient generalization ability to achieve the optimal forecasting of solar irradiance. This paper proposes a novel cooperative multi-input multilayer perceptron neural network (CMMLPNN) to mitigate the issues related to generalization and meteorological effects. Authors develop a proposed forecasting neural network model based on the amalgamation of two inputs, three inputs, four inputs, five inputs, and six inputs associated multilayer perceptron neural network. In the proposed forecasting model (CMMLPNN), the authors overcome the variance based on the meteorological parameters. The amalgamation of five multi-input multilayer perceptron neural networks leads to better generalization ability. Some individual multilayer perceptron neural network-based forecasting models outperform in some situations, but cannot assure generalization ability and suffer from the meteorological weather condition. The proposed CMMLPNN (cooperative multi-input multilayer perceptron neural network) achieves better forecasting accuracy with the generalization ability. Therefore, the proposed forecasting model is superior to other neural network-based forecasting models and existing models.
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Rudenko, Oleg, Oleksandr Bezsonov, and Oleksandr Romanyk. "Neural network time series prediction based on multilayer perceptron." Development Management 17, no. 1 (2019): 23–34. http://dx.doi.org/10.21511/dm.5(1).2019.03.
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Until recently, the statistical approach was the main technique in solving the prediction problem. In the framework of static models, the tasks of forecasting, the identification of hidden periodicity in data, analysis of dependencies, risk assessment in decision making, and others are solved. The general disadvantage of statistical models is the complexity of choosing the type of the model and selecting its parameters. Computing intelligence methods, among which artificial neural networks should be considered at first, can serve as alternative to statistical methods. The ability of the neural network to comprehensively process information follows from their ability to generalize and isolate hidden dependencies between input and output data. Significant advantage of neural networks is that they are capable of learning and generalizing the accumulated knowledge. The article proposes a method of neural networks training in solving the problem of prediction of the time series. Most of the predictive tasks of the time series are characterized by high levels of nonlinearity and non-stationary, noisiness, irregular trends, jumps, abnormal emissions. In these conditions, rigid statistical assumptions about the properties of the time series often limit the possibilities of classical forecasting methods. The alternative methods to statistical methods can be the methods of computational intelligence, which include artificial neural networks. The simulation results confirmed that the proposed method of training the neural network can significantly improve the prediction accuracy of the time series.
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Pukach, A. I., and V. M. Teslyuk. "SUBJECTIVE PERCEPTION MODEL OF SOFTWARE COMPLEXES SUPPORT OBJECT WITH THE ENCAPSULATION OF A MULTILAYER PERCEPTRON ARTIFICIAL NEURAL NETWORKS." Ukrainian Journal of Information Technology 6, no. 2 (2024): 1–10. https://doi.org/10.23939/ujit2024.02.001.
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The object of research in this article – is the process of subjective perception of supported software complexes or their support processes by relevant human entities directly or indirectly interacting with these supported software complexes. Subjective perception model of the software complexes support object with the possibility of encapsulation of artificial neural networks, in particular – a multilayer perceptron, has been developed. Developed model provides possibility to perform modelling of the subjective perception processes of support objects (both the supported software complex itself and the processes of its support) – as one of the important scientific and applied tasks in the direction of scientific and applied problem of software complexes support automation. The developed model general concept provides possibility of artificial neural networks (of all existing types) encapsulation inside the model. In particular, this article considers the encapsulation of the multilayer perceptron type artificial neural network as an example. This paper also considers the main requirements and questions regarding the correspondence, correctness and completeness of the encapsulated multilayer perceptron artificial neural network into the developed model of subjective perception. The developed model is a universal tool that provides possibility to interpret the subjective perceptions of any researchable objects (not only software complexes), and the provided possibility of artificial neural networks encapsulation ensures the possibility of using all the advantages of artificial intelligence, including: increasing the level of automation and intellectualization of modelling process, as well as providing the opportunity for its learning. The result of model development – is a clearly structured and formalized (within the framework of the developed model, presented in this article) process (and the result of this process) of the subjective perception of researched object – the supported software complex, or its support processes. The developed model of subjective perception provides possibilities for resolving a lot of applied practical problems, among which, as an example, this work demonstrates usage of the developed model to solve the practical problem of creating the averaged (general) portrait of the software complex support team.
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Serhiienko, A. V., and E. A. Kolomoichenko. "Study of handwritten character recognition algorithms for different languages using the KAN Neural Network Model." Reporter of the Priazovskyi State Technical University. Section: Technical sciences 1, no. 49 (2024): 36–47. https://doi.org/10.31498/2225-6733.49.1.2024.321184.
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The paper analyzed the most effective existing methods of optical character recognition that use deep learning neural networks in their structure. The analysis revealed that modern neural network architectures with the best recognition accuracy indicators have a constant accuracy limit. It was also found that each analyzed neural network architecture contains a multilayer perceptron in its structure. To optimize the recognition performance of neural networks, it was proposed to use the Kolmogorov-Arnold network as an alternative to multilayer perceptron based networks. The architecture of the created model is based on a two-component transformer, the first component is a visual transformer used as an encoder, the second is a language transformer used as a decoder. The Kolmogorov-Arnold network replaces the feedforward network based on a multilayer perceptron, in each transformer – encoder and decoder. Improvement of existing neural network results is ensured through transfer learning, for which group rational functions are used as the main learning elements of the Kolmogorov-Arnold network. The model was trained on sets of images of text lines from three different writing systems: alphabetic, abugida and logographic; which are represented by the scripts: English, Devanagari and Chinese. As a result of experimental studies, high character recognition rates were found for the Chinese and Devanagari data sets but low for the English script, for the model with the Kolmogorov-Arnold network. The obtained results indicate new possibilities for increasing the reliability and efficiency of modern handwriting recognition systems
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Al-Hroot, Yusuf Ali. "A Comparison of Jordanian Bankruptcy Models: Multilayer Perceptron Neural Network and Discriminant Analysis." International Business Research 9, no. 12 (2016): 121. http://dx.doi.org/10.5539/ibr.v9n12p121.
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<p>The main purpose of this study is to develop and compare the classification accuracy of bankruptcy prediction models using the multilayer perceptron neural network, and discriminant analysis, for the industrial sector in Jordan. The models were developed using the ten popular financial ratios found to be useful in earlier studies and expected to predict bankruptcy. The study sample was divided into two samples; the original sample (n=14) for developing the two models and a hold-out sample (n=18) for testing the prediction of models for three years prior to bankruptcy during the period from 2000 to 2014.</p><p>The results indicated that there was a difference in prediction accuracy between models in two and three years prior to failure. The results indicated that the multilayer perceptron neural network model achieved a higher overall classification accuracy rate for all three years prior to bankruptcy than the discriminant analysis model. Furthermore, the prediction rate was 94.44% two years prior to bankruptcy using multilayer perceptron neural network model and 72.22% using the discriminant analysis model. This is a significant difference of 22.22%. On the other side, the prediction rate of 83.34% three years prior to bankruptcy using multilayer perceptron neural network model and 61.11% using discriminant analysis model. We indicate there was a difference exists of 22.23%. In addition, the multilayer perceptron neural network model provides in the first two years prior to bankruptcy the lowest percentage of type I error.</p>
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Li, Yong, Qidan Zhu, and Ahsan Elahi. "Quadcopter Trajectory Tracking Based on Model Predictive Path Integral Control and Neural Network." Drones 9, no. 1 (2024): 9. https://doi.org/10.3390/drones9010009.
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This paper aims to address the trajectory tracking problem of quadrotors under complex dynamic environments and significant fluctuations in system states. An adaptive trajectory tracking control method is proposed based on an improved Model Predictive Path Integral (MPPI) controller and a Multilayer Perceptron (MLP) neural network. The technique enhances control accuracy and robustness by adjusting control inputs in real time. The Multilayer Perceptron neural network can learn the dynamics of a quadrotor by its state parameter and then the Multilayer Perceptron sends the model to the Model Predictive Path Integral controller. The Model Predictive Path Integral controller uses the model to control the quadcopter following the desired trajectory. Experimental data show that the improved Model Predictive Path Integral–Multilayer Perceptron method reduces the trajectory tracking error by 10.3%, 9.8%, and 5.7% compared to the traditional Model Predictive Path Integral, MPC with MLP, and a two-layer network, respectively. These results demonstrate the potential application of the method in complex environments.
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Khan, Mohd Jawad Ur Rehman, and Anjali Awasthi. "Machine learning model development for predicting road transport GHG emissions in Canada." WSB Journal of Business and Finance 53, no. 2 (2019): 55–72. http://dx.doi.org/10.2478/wsbjbf-2019-0022.
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Abstract Prediction of greenhouse gas (GHG) emissions is important to minimise their negative impact on climate change and global warming. In this article, we propose new models based on data mining and supervised machine learning algorithms (regression and classification) for predicting GHG emissions arising from passenger and freight road transport in Canada. Four models are investigated, namely, artificial neural network multilayer perceptron, multiple linear regression, multinomial logistic regression and decision tree models. From the results, it was found that artificial neural network multilayer perceptron model showed better predictive performance over other models. Ensemble technique (Bagging & Boosting) was applied on the developed multilayer perceptron model, which significantly improved the model’s predictive performance.
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Maca, Petr, and Pavel Pech. "Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks." Computational Intelligence and Neuroscience 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/3868519.
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The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.
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Akbar Maulana and Enny Itje Sela. "The Implementation of Artificial Neural Networks for Stock Price Prediction." Journal of Engineering, Electrical and Informatics 3, no. 3 (2023): 34–44. http://dx.doi.org/10.55606/jeei.v3i3.2254.
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This research is based on a problem that is difficult to predict stock prices, especially for beginners. Stock prices are hard to predict because they are fluctuating. Users will be easier to predict stock prices through artificial neural networks using Multilayer Perceptron. This MLP is a variant of an artificial neural network and is a development of perceptron. The selection of the Multilayer Perceptron method is based on the ability to solve various problems both classification and regression. The research conducted by the author is a regression problem as the MLP is tasked to predict the close price or closing price of stock after seven days. The results of the model built are able to predict stock prices and produce good accuracy because the resulting RMSE value produced 0.042649862994352014, which is close to 0.
 
 Keywords: Machine Learning, Stock Price Prediction, Neural Network, Multilayer Perceptron, MLP.
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Buevich, A. G., I. E. Subbotina, A. V. Shichkin, A. P. Sergeev, and E. M. Baglaeva. "Prediction of the chrome distribution in subarctic Noyabrsk using co-kriging, generalized regression neural network, multilayer perceptron, and hybrid technics." Геоэкология. Инженерная геология. Гидрогеология. Геокриология, no. 2 (May 18, 2019): 77–86. http://dx.doi.org/10.31857/s0869-78092019277-86.
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Combination of geostatistical interpolation (kriging) and machine learning (artificial neural networks, ANN) methods leads to an increase in the accuracy of forecasting. The paper considers the application of residual kriging of an artificial neural network to predicting the spatial contamination of the surface soil layer with chromium (Cr). We reviewed and compared two neural networks: the generalized regression neural network (GRNN) and multilayer perceptron (MLP), as well as the combined method: multilayer perceptron residual kriging (MLPRK). The study is based on the results of the screening of the surface soil layer in the subarctic Noyabrsk, Russia. The models are developed based on computer modeling with minimization of the RMSE. The MLPRK model showed the best prognostic accuracy.
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Ali, Zulifqar, Ijaz Hussain, Muhammad Faisal, et al. "Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model." Advances in Meteorology 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5681308.
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These days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the country’s environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, early measurement and detection of drought can provide guidance to water resources management for employing drought mitigation policies. In this paper, we used a multilayer perceptron neural network (MLPNN) algorithm for drought forecasting. We applied and tested MLPNN algorithm on monthly time series data of Standardized Precipitation Evapotranspiration Index (SPEI) for seventeen climatological stations located in Northern Area and KPK (Pakistan). We found that MLPNN has potential capability for SPEI drought forecasting based on performance measures (i.e., Mean Average Error (MAE), the coefficient of correlation (R), and Root Mean Square Error (RMSE)). Water resources and management planner can take necessary action in advance (e.g., in water scarcity areas) by using MLPNN model as part of their decision-making.
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Rodríguez-Alcántara, Josué U., Adrián Pozos-Estrada, and Roberto Gómez-Martinez. "Use of Artificial Neural Networks to Predict Wind-Induced External Pressure Coefficients on a Low-Rise Building: A Comparative Study." Advances in Civil Engineering 2022 (September 5, 2022): 1–14. http://dx.doi.org/10.1155/2022/8796384.
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Wind flow on a bluff body is a complex and nonlinear phenomenon that has been mainly studied experimentally or analytically. Several mathematical methods have been developed to predict the wind-induced pressure distribution on bluff bodies; however, most of them result unpractical due to the mathematical complexity required. Long-short term memory artificial neural networks with deep learning have proven to be efficient tools in the solution of nonlinear phenomena, although the choice of a more efficient network model remains a topic of open discussion for researchers. The main objective of this study is to develop long-short term memory artificial neural network models to predict the external pressure distribution of a low-rise building. For the development of the artificial neural network models, the multilayer perceptron and the recurrent neural network were also employed for comparison purposes. To train the artificial neural networks, a database with the external pressure coefficients from boundary layer wind tunnel tests of a low-rise building is employed. The analysis results indicate that the long-short term memory artificial neural network model and the multilayer perceptron neural network outperform the recurrent neural network.
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Babenko, Tetyana, Andrii Bigdan, and Larisa Myrutenko. "Intelligent model of classification of network cyber security events." Information systems and technologies security, no. 1 (6) (2023): 61–69. http://dx.doi.org/10.17721/ists.2023.1.61-69.
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Due to the increased complexity of modern computer attacks, there is a need for security professionals not only to detect harmful activity but also to determine the appropriate steps that an attacker will go through when performing an attack. Even though the detection of exploits and vulnerabilities is growing every day, the development of protection methods is progressing much more slowly than attack methods. Therefore, this remains an open research problem. In this article, we present our research in network attack identification using neural networks, in particular Rumelhart's multilayer perceptron, to identify and predict future network security events based on previous observations. To ensure the quality of the training process and obtain the desired generalization of the model, 4 million records accumulated over 7 days by the Canadian Cybersecurity Institute were used. Our result shows that neural network models based on a multilayer perceptron can be used after refinement to detect and predict network security events.
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Lerro, Angelo, Piero Gili, Mario Luca Fravolini, and Marcello Napolitano. "Experimental Analysis of Neural Approaches for Synthetic Angle-of-Attack Estimation." International Journal of Aerospace Engineering 2021 (July 9, 2021): 1–13. http://dx.doi.org/10.1155/2021/9982722.
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Synthetic sensors enable flight data estimation without devoted physical sensors. Within modern digital avionics, synthetic sensors can be implemented and used for several purposes such as analytical redundancy or monitoring functions. The angle of attack, measured at air data system level, can be estimated using synthetic sensors exploiting several solutions, e.g., model-based, data-driven, and model-free state observers. In the class of data-driven observers, multilayer perceptron neural networks are widely used to approximate the input-output mapping angle-of-attack function. Dealing with experimental flight test data, the multilayer perceptron can provide reliable estimation even though some issues can arise from noisy, sparse, and unbalanced training domain. An alternative is offered by regularization networks, such as radial basis function, to cope with training domain based on real flight data. The present work’s objective is to evaluate performances of a single-layer feed-forward generalized radial basis function network for AoA estimation trained with a sequential algorithm. The proposed analysis is performed comparing results obtained using a multilayer perceptron network adopting the same training and validation data.
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Mirzakhani, Farzad. "Detection of Lung Cancer using Multilayer Perceptron Neural Network." Medical Technologies Journal 1, no. 4 (2017): 109. http://dx.doi.org/10.26415/2572-004x-vol1iss4p109.
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Introduction: Lung cancer is the most common cancer in terms of prevalence and mortality. The cancer can be detected once it is reached to a stage that is visible in the CT imaging. Eighty six percent of the patients with lung cancer because they are late understand their disease, surgery has little effect on their improvement. Therefore, the existence of an intelligent system that can detect lung cancer in the early stages is necessary.
 Methods: In this study, a lung cancer dataset of UCI database was used. This dataset consists of 32 samples, 57 variables and 3 classes (each class including 10, 9 and 13 samples). The data were normalized within the range 0 to 1. Then, to increase the detection speed, the dimensions of the data were reduced by using the Principal Components Analysis (PCA). Then, using a multilayer perceptron neural network, a model for classification and prediction of lung cancer was developed. Finally, the performance of the model was measured using sensitivity, specificity, positive predictive value and negative predictive value. It should be noted that all analyzes were done using Weka software.
 Results: After developing and evaluating an artificial neural network model, the developed model had a sensitivity of 66.7%, a 98.5% specificity, a positive predictive value of 75%, and a negative predictive value of 97.7%.
 Conclusion: In intelligent diagnostic systems, in addition to high accuracy of diagnosis, the speed of diagnosis and decision making is also important. Therefore, researchers increased the speed of the prediction model by reducing 57 variables to 8 variables using PCA. Also, the high sensitivity and high specificity of developed model demonstrates high power of artificial neural network model in detecting lung cancer.
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Kalinchyk, Vasyl, Olexandr Meita, Vitalii Pobigaylo, Vitalii Pobigaylo, Olena Borychenko, and Vitalii Kalinchyk. "Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex." Rocznik Ochrona Środowiska 24 (2022): 26–40. http://dx.doi.org/10.54740/ros.2022.003.
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This article investigates the application of neural network models to create automated control systems for industrial processes. We reviewed and analysed works on dispatch control and evaluation of equipment operating modes, and the use of artificial neural networks to solve problems of this type. It is shown that the main requirements for identification models are the accuracy of estimation and ease of algorithm implementation. It is shown that artificial neural networks meet the requirements for accuracy of classification problems, ease of execution and speed. We considered the structures of neural networks that can be used to recognize the modes of operation of technological equipment. Application of the model and structure of networks with radial basis functions and multilayer perceptrons for the tasks of identifying the mode of operation of equipment under given conditions is substantiated. The input conditions for the construction of neural network models of two types with a given three-layer structure are offered. The results of training neural models on the model of a multilayer perceptron and a network with radial basis functions are presented. The estimation and comparative analysis of models depending on model parameters is made. It is shown that networks with radial basis functions offer greater accuracy in solving identification problems. The structural scheme of the automated process control system with mode identification on the basis of artificial neural networks is offered.
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Sanjar, Zokirov, та Abdumanonov Ahrorjon. "KASALLIKLARNI TASHXISLASH QARORLARINI QABUL QILISH TIZIMLARIDA NEYRON TARMOQLARNI OʻQITISH ALGORITMLARI". Scientific-technical journal, спец.выпуск №3 (1 січня 2023): 49–56. https://doi.org/10.5281/zenodo.7562467.
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This article presents an analysis of the use of artificial neural network technologies in the medical diagnosis of diseases, the purpose of which is to determine which areas of diagnosis using neural network technologies are the most effective, as well as the effectiveness of learning system algorithms. At the same time, the structure of artificial neural networks, learning algorithms and the accuracy of the functioning of artificial neural networks were considered. In the article it was found that the most optimal model of artificial neural networks for solving problems of medical diagnostics is a multilayer perceptron, which is a direct propagation network in which neurons of one layer are sequentially connected to neurons of adjacent layers without recurrent connections, it was revealed that the most optimal algorithms for training a multilayer perceptron are an error back propagation algorithm and a genetic algorithm. The introduction of neural networks of diagnostic models into clinical practice can provide effective assistance in making medical decisions, improve the quality and accuracy of diagnosis of diseases
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Tran, Thanh Ngoc, Van Dai Le, and Thi Phuc Dang. "Grid search of multilayer perceptron based on the walkforward validation methodology." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1742–51. https://doi.org/10.11591/ijece.v11i2.pp1742-1751.
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Multilayer perceptron neural network is one of the widely used method for load forecasting. There are hyperparameters which can be used to determine the network structure and used to train the multilayer perceptron neural network model. This paper aims to propose a framework for grid search model based on the walk-forward validation methodology. The training process will specify the optimal models which satisfy requirement for minimum of accuracy scores of root mean square error, mean absolute percentage error and mean absolute error. The testing process will evaluate the optimal models along with the other ones. The results indicated that the optimal models have accuracy scores near the minimum values. The US airline passenger and Ho Chi Minh city load demand data were used to verify the accuracy and reliability of the grid search framework.
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Emedolu, Blessing Obianuju, Godwin Thomas, and Nentawe Y. Gurumdimma. "Phishing Website Detection using Multilayer Perceptron." International Journal of Research and Innovation in Applied Science VIII, no. VII (2023): 260–67. http://dx.doi.org/10.51584/ijrias.2023.8730.
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Phishing attacks pose a significant threat in the cyber world, exploiting unsuspecting users through deceptive emails that lead them to malicious websites. To combat this challenge, various deep learning based anti-phishing techniques have been developed. However, these models often suffer from high false positive rates or lower accuracy. In this study, we evaluate the performance of two neural networks, the Autoencoder and Multilayer Perceptron (MLP), using a publicly available dataset to build an efficient phishing detection model. Feature selection was performed through correlation analysis, and the Autoencoder achieved an accuracy of 94.17%, while the MLP achieved 96%. We used hyperparameters for optimization using the Gridsearch CV, resulting in a False Positive Rate (FPR) of 1.3%, outperforming the Autoencoder’s 4.1% FPR. The MLP model was further deployed to determine the legitimacy of websites based on input URLs, demonstrating its usability in real-world scenarios. This research contributes to the development of effective phishing detection models, emphasizing the importance of optimizing neural network architecture for improved accuracy and reduced false positives
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Al-Hroot, Yusuf Ali Khalaf. "Bankruptcy Prediction Using Multilayer Perceptron Neural Networks In Jordan." European Scientific Journal, ESJ 12, no. 4 (2016): 425. http://dx.doi.org/10.19044/esj.2016.v12n4p425.
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This study attempts to develop bankruptcy prediction model for the Jordanian industrial sector with a recent approach—neural networks. The multilayer perceptron neural network (MPNN) approach was used to develop the bankruptcy prediction model for the Jordanian industrial companies for the period from 2000 to 2015. The samples have been divided into two subsets: the first set for developing or building the model, made up of 14 companies, of which 7 are bankrupt and 7 are non-bankrupt; while the second is a hold-out sample for testing the model, made up of 18 companies, of which 9 are bankrupt and 9 are non-bankrupt. The main variables in predicting bankruptcy were ten financial ratios. The results show that the accuracy rate of final prediction model is found to be 100 percent. While the hold-out sample testing provides that the model correctly predicted all 18 test cases.
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Shafi, Numan, Faisal Bukhari, Waheed Iqbal, Khaled Mohamad Almustafa, Muhammad Asif, and Zubair Nawaz. "Cleft prediction before birth using deep neural network." Health Informatics Journal 26, no. 4 (2020): 2568–85. http://dx.doi.org/10.1177/1460458220911789.
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In developing countries like Pakistan, cleft surgery is expensive for families, and the child also experiences much pain. In this article, we propose a machine learning–based solution to avoid cleft in the mother’s womb. The possibility of cleft lip and palate in embryos can be predicted before birth by using the proposed solution. We collected 1000 pregnant female samples from three different hospitals in Lahore, Punjab. A questionnaire has been designed to obtain a variety of data, such as gender, parenting, family history of cleft, the order of birth, the number of children, midwives counseling, miscarriage history, parent smoking, and physician visits. Different cleaning, scaling, and feature selection methods have been applied to the data collected. After selecting the best features from the cleft data, various machine learning algorithms were used, including random forest, k-nearest neighbor, decision tree, support vector machine, and multilayer perceptron. In our implementation, multilayer perceptron is a deep neural network, which yields excellent results for the cleft dataset compared to the other methods. We achieved 92.6% accuracy on test data based on the multilayer perceptron model. Our promising results of predictions would help to fight future clefts for children who would have cleft.
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Georgios, Rigopoulos. "Multilayer Perceptron model efficacy for S&P 500 Stock Option Pricing." International Journal of Computer Science and Information Technology Research 11, no. 3 (2023): 153–59. https://doi.org/10.5281/zenodo.8338714.
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<strong>Abstract:</strong> Option pricing is of key importance for stock markets and traders to reduce risk, avoid loss and on the other hand speculate on stock price movements. This work explores the efficacy of using artificial neural network approach in call option pricing. We built a multilayer perceptron model trained it with real market option contracts data and tested it in option data originated from fifty S&P 500 stocks. In our approach both training and testing data are market oriented and this is a unique contribution to existing research, where training is usually based on artificially generated data. Our findings demonstrate that multilayer perceptron performs very well in actual market data and is competitive to Black-Scholes pricing formula. Further exploration and experimentation is required, however, so machine learning approaches reach required robustness and become less ad hoc and data sensitive. Despite its limitations, it is a very promising approach and can play a substantial role in option pricing, provided that it is supported by relevant software solutions. <strong>Keywords:</strong> stock option pricing, artificial neural network, multilayer perceptron. <strong>Title:</strong> Multilayer Perceptron model efficacy for S&P 500 Stock Option Pricing <strong>Author:</strong> Georgios Rigopoulos <strong>International Journal of Computer Science and Information Technology Research</strong> <strong>ISSN 2348-1196 (print), ISSN 2348-120X (online)</strong> <strong>Vol. 11, Issue 3, July 2023 - September 2023</strong> <strong>Page No: 153-159</strong> <strong>Research Publish Journals</strong> <strong>Website: www.researchpublish.com</strong> <strong>Published Date: 12-September-2023</strong> <strong>DOI: https://doi.org/10.5281/zenodo.8338714</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.researchpublish.com/papers/multilayer-perceptron-model-efficacy-for-sp-500-stock-option-pricing</strong>
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Du, Ke-Lin, Chi-Sing Leung, Wai Ho Mow, and M. N. S. Swamy. "Perceptron: Learning, Generalization, Model Selection, Fault Tolerance, and Role in the Deep Learning Era." Mathematics 10, no. 24 (2022): 4730. http://dx.doi.org/10.3390/math10244730.
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The single-layer perceptron, introduced by Rosenblatt in 1958, is one of the earliest and simplest neural network models. However, it is incapable of classifying linearly inseparable patterns. A new era of neural network research started in 1986, when the backpropagation (BP) algorithm was rediscovered for training the multilayer perceptron (MLP) model. An MLP with a large number of hidden nodes can function as a universal approximator. To date, the MLP model is the most fundamental and important neural network model. It is also the most investigated neural network model. Even in this AI or deep learning era, the MLP is still among the few most investigated and used neural network models. Numerous new results have been obtained in the past three decades. This survey paper gives a comprehensive and state-of-the-art introduction to the perceptron model, with emphasis on learning, generalization, model selection and fault tolerance. The role of the perceptron model in the deep learning era is also described. This paper provides a concluding survey of perceptron learning, and it covers all the major achievements in the past seven decades. It also serves a tutorial for perceptron learning.
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Özerk, Yavuz, Karahoca Adem, and Karahoca Dilek. "A data mining approach for desire and intention to participate in virtual communities." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3714–19. https://doi.org/10.11591/ijece.v9i5.pp3714-3719.
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The purpose of this study is to investigate performances of some of the data mining approaches while understanding desire and intention to participate in virtual communities and its antecedents. A research model has been developed following the literature review and the model was tested afterwards. In research part of the study, some of the data mining approaches as JRip, Part, OneR Method, Multilayer Perceptron (Neural Networks), Bayesian Networks have been used. Based on the analysis conducted it has been found out that Multilayer Neural Network had the best correct classification rate and lowest RMSE.
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Pukach, A. I., and V. M. Teslyuk. "DEVELOPMENT A MULTIFACTORIAL PORTRAIT MODEL OF SOFTWARE COMPLEXES’ SUPPORTING SUBJECTS, USING ARTIFICIAL NEURAL NETWORKS." Computer systems and network 6, no. 2 (2024): 192–203. https://doi.org/10.23939/csn2024.02.192.
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Impact factors, that are shaping the individualistic perception of the supported objects by the relevant subjects, who interact with them, directly or indirectly, are considered in this research. A form of impact factors’ (performing impact on the supported software complexes) representation has been studied and proposed. Aforementioned form includes: a set of input characteristics of the researched supported object; a set of impact factors in the form of a transformation matrix function; and a set of output characteristics of the resulting perception of the same researched supported object (however in the individualistic perception of each individual subject of interaction with it). The possibility of encapsulation of artificial neural networks inside the aforementioned proposed form (of the supported software complexes’ impact factors representation) was investigated. And the use of multilayer perceptron was proposed and substantiated for the implementation of the appropriate encapsulations. An appropriate multifactorial portrait model of software complexes’ supporting subjects, using artificial neural networks, particularly a multilayer perceptron, has been developed and presented. Also, the applied practical problem of determining the deficient impact factors for each of the software complex’ support team members has been solved. Key words: model, artificial neural networks, multilayer perceptron, impact factors, multifactor portrait, software complexes, support, automation.
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Pukach, A. I., and V. M. Teslyuk. "DEVELOPMENT A MULTIFACTORIAL PORTRAIT MODEL OF SOFTWARE COMPLEXES’ SUPPORTING SUBJECTS, USING ARTIFICIAL NEURAL NETWORKS." Computer systems and network 6, no. 2 (2024): 197–207. https://doi.org/10.23939/csn2024.02.197.
Full textAbstract:
Impact factors, that are shaping the individualistic perception of the supported objects by the relevant subjects, who interact with them, directly or indirectly, are considered in this research. A form of impact factors’ (performing impact on the supported software complexes) representation has been studied and proposed. Aforementioned form includes: a set of input characteristics of the researched supported object; a set of impact factors in the form of a transformation matrix function; and a set of output characteristics of the resulting perception of the same researched supported object (however in the individualistic perception of each individual subject of interaction with it). The possibility of encapsulation of artificial neural networks inside the aforementioned proposed form (of the supported software complexes’ impact factors representation) was investigated. And the use of multilayer perceptron was proposed and substantiated for the implementation of the appropriate encapsulations. An appropriate multifactorial portrait model of software complexes’ supporting subjects, using artificial neural networks, particularly a multilayer perceptron, has been developed and presented. Also, the applied practical problem of determining the deficient impact factors for each of the software complex’ support team members has been solved. Key words: model, artificial neural networks, multilayer perceptron, impact factors, multifactor portrait, software complexes, support, automation.
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Zambri, N. A., Norhafiz Salim, A. Mohamed, and Ili Najaa Aimi Mohd Nordin. "Modeling of a planar SOFC performances using artificial neural network." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 3 (2019): 1645. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1645-1652.
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The Planar Solid Oxide Fuel Cell (PSOFC) is one of the renewable energy technologies that is important as the main source for distributed generation and can play a significant role in the conventional electrical power generation. PSOFC stack modeling is performed in order to provide a platform for the optimal design of fuel cell systems. It is explained by the structure and operating principle of the PSOFC for the modeling purposes. PSOFC model can be developed using Artificial Neural Network approach. The data required to train the neural net-work model is generated by simulating the existing PSOFC model in the MATLAB/ Simulink software. The Radial Basis Function (RBF) and Multilayer Perceptron (MLP) neural networks are the most useful techniques in many applications and will be applied in developing the PSOFC model. A detailed analysis is presented on the best ANN network that gives the greatest results on the performances of the PSOFC. The simulation results show that Multilayer Perceptron (MLP) gives the best outcomes of the PSOFC performance based on the smallest errors and good regression analysis.
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Anggraeni, Dewi, and Sri Rezki Maulina Azmi. "ANALYSIS OF NEURAL NETWORK ALGORITHM IN URBAN AIR QUALITY PREDICTION." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 11, no. 2 (2025): 375–80. https://doi.org/10.33330/jurteksi.v11i2.3822.
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Abstract: Air quality in urban areas is becoming an increasingly important issue considering its impact on human health and the environment. The rapid increase in air pollution requires effective methods to predict air quality in order to take appropriate mitigation measures. This study aims to analyze the use of Neural Network (NN) algorithms in predicting air quality in cities. The method used is the application of the NN model, especially the Multilayer Perceptron (MLP), which is trained using historical air quality data such as dust particle levels (PM10, PM2.5), carbon monoxide (CO) gas, and temperature. The data used in this study came from urban air quality monitoring stations collected over a period of time. The results show that the Neural Network algorithm can provide quite accurate predictions of air quality with a low Mean Absolute Error (MAE) value, showing the effectiveness of the model in predicting f fluctuations in air quality. The conclusion of this study is that Neural Network algorithms, specifically MLPs, are an effective tool for air quality prediction, which can be used as a basis for urban air quality management policies. Keywords: air quality; neural network; prediction; multilayer perceptron (MLP) Abstrak: Kualitas udara di perkotaan menjadi isu yang semakin penting mengingat dampaknya terhadap kesehatan manusia dan lingkungan. Peningkatan polusi udara yang pesat memerlukan metode yang efektif untuk memprediksi kualitas udara guna mengambil langkah mitigasi yang tepat. Penelitian ini bertujuan untuk menganalisis penggunaan algoritma Neural Network (NN) dalam memprediksi kualitas udara di perkotaan. Metode yang digunakan adalah penerapan model NN, khususnya Multilayer Perceptron (MLP), yang dilatih menggunakan data kualitas udara historis seperti kadar partikel debu (PM10, PM2.5), gas karbon monoksida (CO), dan suhu. Data yang digunakan dalam penelitian ini berasal dari stasiun pemantauan kualitas udara di perkotaan yang dikumpulkan selama periode waktu tertentu. Hasil penelitian menunjukkan bahwa algoritma Neural Network dapat memberikan prediksi yang cukup akurat terhadap kualitas udara dengan nilai Mean Absolute Error (MAE) yang rendah, menunjukkan efektivitas model dalam memprediksi fluktuasi kualitas udara. Simpulan dari penelitian ini adalah bahwa algoritma Neural Network, khususnya MLP, merupakan alat yang efektif untuk prediksi kualitas udara, yang dapat digunakan sebagai dasar untuk kebijakan pengelolaan kualitas udara di perkotaanKata kunci: kualitas udara; neural network; prediksi; multilayer perceptron (MLP)
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Govindarajan, M., and RM Chandrasekaran. "A Hybrid Multilayer Perceptron Neural Network for Direct Marketing." International Journal of Knowledge-Based Organizations 2, no. 3 (2012): 63–73. http://dx.doi.org/10.4018/ijkbo.2012070104.
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Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in database process. It is often referred to as supervised learning because the classes are determined before examining the data. In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes feature selection and model selection simultaneously for Multilayer Perceptron (MLP) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the classifier significantly. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: Direct Marketing in Customer Relationship Management. It is shown that, compared to earlier MLP technique, the run time is reduced with respect to learning data and with validation data for the proposed Multilayer Perceptron (MLP) classifiers. Similarly, the error rate is relatively low with respect to learning data and with validation data in direct marketing dataset. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.
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Ajoku, Kingsley Kelechi, O. C. Nwokonkwo, A. M. John-Otumu, and Chukwuemeka Philips Oleji. "A Model for Stock Market Value Forecasting using Ensemble Artificial Neural Network." Journal of Advances in Computing, Communications and Information Technology 2 (December 31, 2021): 1–13. http://dx.doi.org/10.37121/jaccit.v2.162.
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Artificial Neural Network (ANN) is a model used in capturing linear and non-linear relationship of input and output data. Its usage has been predominant in the prediction and forecasting market time series. However, there has been low bias and high variance issues associated with ANN models such as the simple multi-layer perceptron model. This usually happens when training large dataset. The objective of this work was to develop an efficient forecasting model using Ensemble ANN to unravel the market mysteries for accurate decision on investment. This paper employed the Ensemble ANN modeling technique to tackle the high variations in stock market training dataset faced when using a simple multi-layer perceptron model by using the theory of ensemble averaging. The Ensemble ANN model was developed and implemented using NeurophStudio and Java programming language, then trained and tested using daily data of stock market prices from various banks, for a period of 497 days. The methodology adopted to achieve this task is the agile methodology. The output of the proposed predictive model was compared with four traditional neural network multilayer perceptron algorithms, and outperformed the traditional neural network multilayer perceptron algorithms. The proposed model gave an average to best predictive error for any day when compared with the other four traditional models.
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Kovács, László. "Classification Improvement with Integration of Radial Basis Function and Multilayer Perceptron Network Architectures." Mathematics 13, no. 9 (2025): 1471. https://doi.org/10.3390/math13091471.
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The radial basis function architecture and the multilayer perceptron architecture are very different approaches to neural networks in theory and practice. Considering their classification efficiency, both have different strengths; thus, the integration of these tools is an interesting but understudied problem domain. This paper presents a novel initialization method based on a distance-weighted homogeneity measure to construct a radial basis function network with fast convergence. The proposed radial basis function network is utilized in the development of an integrated RBF-MLP architecture. The proposed neural network model was tested in various classification tasks and the test results show superiority of the proposed architecture. The RBF-MLP model achieved nearly 40 percent better accuracy in the tests than the baseline MLP or RBF neural network architectures.
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Wang, Chunzhi, Weidong Cao, Xiaodong Wen, Lingyu Yan, Fang Zhou, and Neal Xiong. "An Intelligent Network Traffic Prediction Scheme Based on Ensemble Learning of Multi-Layer Perceptron in Complex Networks." Electronics 12, no. 6 (2023): 1268. http://dx.doi.org/10.3390/electronics12061268.
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At present, the amount of network equipment, servers, and network traffic is increasing exponentially, and the way in which operators allocate and efficiently utilize network resources has attracted considerable attention from traffic forecasting researchers. However, with the advent of the 5G era, network traffic has also shown explosive growth, and network complexity has increased dramatically. Accurately predicting network traffic has become a pressing issue that must be addressed. In this paper, a multilayer perceptron ensemble learning method based on convolutional neural networks (CNN) and gated recurrent units (GRU) spatiotemporal feature extraction (MECG) is proposed for network traffic prediction. First, we extract spatial and temporal features of the data by convolutional neural networks (CNN) and recurrent neural networks (RNN). Then, the extracted temporal features and spatial features are fused into new spatiotemporal features through integrated learning of a multilayer perceptron, and a spatiotemporal prediction model is built in the sequence-to-sequence framework. At the same time, the teacher forcing mechanism and attention mechanism are added to improve the accuracy and convergence speed of the model. Finally, the proposed method is compared with other deep learning models for experiments. The experimental results show that the proposed method not only has apparent advantages in accuracy but also shows some superiority in time training cost.
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Lukito, Yuan. "Multi Layer Perceptron Model for Indoor Positioning System Based on Wi-Fi." Jurnal Teknologi dan Sistem Komputer 5, no. 3 (2017): 123–28. http://dx.doi.org/10.14710/jtsiskom.5.3.2017.123-128.
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Indoor positioning system issue is an open problem that still needs some improvements. This research explores the utilization of multilayer perceptron in determining someone’s position inside a building or a room, which generally known as Indoor Positioning System. The research was conducted in some steps: dataset normalization, multilayer perceptron implementation, training process of multilayer perceptron, evaluation, and analysis. The training process has been conducted many times to find the best parameters that produce the best accuracy rate. The experiment produces 79,16% as the highest accuracy rate. Compared to previous research, this result is comparably lower and needs some parameters tweaking or changing the neural networks architectures.
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Akishin, P. G., P. Akritas, I. Antoniou, and V. V. Ivanov. "Identification of discrete chaotic maps with singular points." Discrete Dynamics in Nature and Society 6, no. 3 (2001): 147–56. http://dx.doi.org/10.1155/s1026022601000164.
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We investigate the ability of artificial neural networks to reconstruct discrete chaotic maps with singular points. We use as a simple test model the Cusp map. We compare the traditional Multilayer Perceptron, the Chebyshev Neural Network and the Wavelet Neural Network. The numerical scheme for the accurate determination of a singular point is also developed. We show that combining a neural network with the numerical algorithm for the determination of the singular point we are able to accurately approximate discrete chaotic maps with singularities.
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Pavlin-Bernardić, Nina, Silvija Ravić, and Ivan Pavao Matić. "The Application of Artificial Neural Networks in Predicting Children’s Giftedness." Suvremena psihologija 19, no. 1 (2016): 49–59. http://dx.doi.org/10.21465/2016-sp-191-04.
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Artificial neural networks have a wide use in the prediction and classification of different variables, but their application in the area of educational psychology is still relatively rare. The aim of this study was to examine the accuracy of artificial neural networks in predicting students’ general giftedness. The participants were 221 fourth grade students from one Croatian elementary school. The input variables for artificial neural networks were teachers’ and peers’ nominations, school grades, earlier school readiness assessment and parents’ education. The output variable was the result on the Standard Progressive Matrices (Raven, 1994), according to which students were classified as gifted or non-gifted. We tested two artificial neural networks’ algorithms: multilayer perceptron and radial basis function. Within each algorithm, a number of different types of activation functions were tested. 80% of the sample was used for training the network and the remaining 20% to test the network. For a criterion according to which students were classified as gifted if their result on the Standard Progressive Matrices was in the 95th centile or above, the best model was obtained by the hyperbolic tangent multilayer perceptron, which had a high accuracy of 100% of correctly classified non-gifted students and 75% correctly classified gifted students in the test sample. When the criterion was the 90th centile or above, the best model was also obtained by the hyperbolic tangent multilayer perceptron, but the accuracy was lower: 94.7% in the classification of non-gifted students and 66.7% in the classification of gifted students. The study has shown artificial neural networks’ potential in this area, which should be further explored. Keywords: gifted students, identification of gifted students, artificial neural networks
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Mahnaz Zameni, Mahdi Ahmadi, and Arash Talebi. "Estimation of the mean effective pressure of a spark ignition internal combustion engine using a neural network, considering the wall-wetting dynamics." Global Journal of Engineering and Technology Advances 19, no. 2 (2024): 010–18. http://dx.doi.org/10.30574/gjeta.2024.19.2.0073.
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The management and development of internal combustion engines stand as critical pursuits within the automotive and related industries. Utilizing cylinder pressure as feedback, engine controllers rely on intricate systems to regulate performance. However, due to the inherent complexity and nonlinearity of engines, direct measurement of cylinder pressure through pressure sensors is costly and computationally demanding. Consequently, the need for accurate and detailed engine models becomes paramount. Neural networks offer a promising avenue for simulating internal combustion engines, combining speed and precision. By treating the engine as an enigmatic entity, neural networks can construct detailed models. This study aims to employ two types of neural networks—multilayer perceptron and radial basis functions—to train and build a model of an internal combustion engine. These networks will simulate and estimate the engine's mean suitable pressure, allowing for a comparison of their effectiveness. Prior to implementing the neural network architecture, an engine model was constructed in MATLAB to gather necessary training data. This preliminary step ensured a robust foundation for subsequent network design and implementation. In summary, this research focuses on leveraging neural networks to model internal combustion engines, utilizing both multilayer perceptron and radial basis functions to simulate engine behavior and estimate mean suitable pressure.
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Mahnaz, Zameni, Ahmadi Mahdi, and Talebi Arash. "Estimation of the mean effective pressure of a spark ignition internal combustion engine using a neural network, considering the wall-wetting dynamics." Global Journal of Engineering and Technology Advances 19, no. 2 (2024): 010–18. https://doi.org/10.5281/zenodo.13691597.
Full textAbstract:
The management and development of internal combustion engines stand as critical pursuits within the automotive and related industries. Utilizing cylinder pressure as feedback, engine controllers rely on intricate systems to regulate performance. However, due to the inherent complexity and nonlinearity of engines, direct measurement of cylinder pressure through pressure sensors is costly and computationally demanding. Consequently, the need for accurate and detailed engine models becomes paramount. Neural networks offer a promising avenue for simulating internal combustion engines, combining speed and precision. By treating the engine as an enigmatic entity, neural networks can construct detailed models. This study aims to employ two types of neural networks—multilayer perceptron and radial basis functions—to train and build a model of an internal combustion engine. These networks will simulate and estimate the engine's mean suitable pressure, allowing for a comparison of their effectiveness. Prior to implementing the neural network architecture, an engine model was constructed in MATLAB to gather necessary training data. This preliminary step ensured a robust foundation for subsequent network design and implementation. In summary, this research focuses on leveraging neural networks to model internal combustion engines, utilizing both multilayer perceptron and radial basis functions to simulate engine behavior and estimate mean suitable pressure.
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Yan, Cong. "Audience Evaluation and Analysis of Symphony Performance Effects Based on the Genetic Neural Network Algorithm for the Multilayer Perceptron (GA-MLP-NN)." Computational Intelligence and Neuroscience 2021 (October 8, 2021): 1–9. http://dx.doi.org/10.1155/2021/4133892.
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Traditional symphony performances need to obtain a large amount of data in terms of effect evaluation to ensure the authenticity and stability of the data. In the process of processing the audience evaluation data, there are problems such as large calculation dimensions and low data relevance. Based on this, this article studies the audience evaluation model of teaching quality based on the multilayer perceptron genetic neural network algorithm for the data processing link in the evaluation of the symphony performance effect. Multilayer perceptrons are combined to collect data on the audience’s evaluation information; genetic neural network algorithm is used for comprehensive analysis to realize multivariate analysis and objective evaluation of all vocal data of the symphony performance process and effects according to different characteristics and expressions of the audience evaluation. Changes are analyzed and evaluated accurately. The experimental results show that the performance evaluation model of symphony performance based on the multilayer perceptron genetic neural network algorithm can be quantitatively evaluated in real time and is at least higher in accuracy than the results obtained by the mainstream evaluation method of data postprocessing with optimized iterative algorithms as the core 23.1%, its scope of application is also wider, and it has important practical significance in real-time quantitative evaluation of the effect of symphony performance.
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Chi, Guotai, Mohammad Shamsu Uddin, Mohammad Zoynul Abedin, and Kunpeng Yuan. "Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches." International Journal on Artificial Intelligence Tools 28, no. 05 (2019): 1950017. http://dx.doi.org/10.1142/s0218213019500179.
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Credit risk prediction is essential for banks and financial institutions as it helps them to evade any inappropriate assessments that can lead to wasted opportunities or monetary losses. In recent times, the hybrid prediction model, a combination of traditional and modern artificial intelligence (AI) methods that provides better prediction capacity than the use of single techniques, has been introduced. Similarly, using conventional and topical artificial intelligence (AI) technologies, researchers have recommended hybrid models which amalgamate logistic regression (LR) with multilayer perceptron (MLP). To investigate the efficiency and viability of the proposed hybrid models, we compared 16 hybrid models created by combining logistic regression (LR), discriminant analysis (DA), and decision trees (DT) with four types of neural network (NN): adaptive neurofuzzy inference systems (ANFISs), deep neural networks (DNNs), radial basis function networks (RBFs) and multilayer perceptrons (MLPs). The experimental outcome, investigation, and statistical examination express the capacity of the planned hybrid model to develop a credit risk prediction technique different from all other approaches, as indicated by ten different performance measures. The classifier was authenticated on five real-world credit scoring data sets.
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Jia, Wendi, and Quanlong Chen. "Aircraft Structural Stress Prediction Based on Multilayer Perceptron Neural Network." Applied Sciences 14, no. 21 (2024): 9995. http://dx.doi.org/10.3390/app14219995.
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In the field of aeronautics, aircraft, as a critical aviation tool, exert a decisive influence on the structural integrity and safety of the entire system. Accurate prediction of the stress field distribution and variations within the aircraft structure is of great importance to ensuring its safety performance. To facilitate such predictions, a rapid assessment method for stress fields based on a multilayer perceptron (MLP) neural network is proposed. Compared to the traditional machine learning algorithm, the random forest algorithm, MLP demonstrates superior accuracy and computational efficiency in stress field prediction, particularly exhibiting enhanced adaptability when handling high-dimensional input data. This method is applied to predict stresses in the wing rib structure. By performing finite element meshing on the wing ribs, the angle of attack, inflow velocity, and node coordinates are utilized as input tensors for the model, enabling it to learn the stress distribution in the wing ribs. Additionally, a peak stress prediction model is separately established for regions experiencing peak stresses. The results indicate that the MAPE of the stress field prediction model is within 5%, with a coefficient of determination R2 exceeding 0.994. For the peak stress model, the MAPE is within 2%, with an R2 exceeding 0.995. This method offers faster computation and greater flexibility, presenting a novel approach for structural strength assessment.
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Senyk, A. P., O. S. Manziy, V. R. Pelekh, Y. V. Futryk, and Y. A. Senyk. "The role of functional activation in neural networks in the context of financial time series analysis." Mathematical Modeling and Computing 12, no. 1 (2025): 299–309. https://doi.org/10.23939/mmc2025.01.299.
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Nowadays, neural networks are among the most popular analysis tools. They are effective in solving classification, pattern recognition, and clustering problems. This paper provides a detailed description and analysis of the operational principles of two neural networks, namely a Siamese network and a multilayer perceptron. A model for using these neural networks in time series forecasting is proposed. As an example, a web application was created in which the described neural networks were used to analyze the correlation between pairs of financial assets and assess the risk level of an investment portfolio. Modern information technologies, visualization methods, and advanced analysis tools used in the developed software product provide users with a comprehensive understanding of their investments, allowing them to assess risks and opportunities, as well as determine strategies for maximizing income and diversifying their selected set of financial assets. The research results demonstrate the effectiveness of the Siamese network and multilayer perceptron in forecasting the prices of financial assets on the stock market and applying the obtained results in investment management tasks.
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Yavuz, Özerk, Adem Karahoca, and Dilek Karahoca. "A data mining approach for desire and intention to participate in virtual communities." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3714. http://dx.doi.org/10.11591/ijece.v9i5.pp3714-3719.
Full textAbstract:
<span lang="EN-US">The purpose of this study is to investigate performances of some of the data mining approaches while understanding desire and intention to participate in virtual communities and its antecedents. A research model has been developed following the literature review and the model was tested afterwards. In research part of the study, some of the data mining approaches as JRip, Part, OneR Method, Multilayer Perceptron (Neural Networks), Bayesian Networks have been used. Based on the analysis conducted it has been found out that Multilayer Neural Network had the best correct classification rate and lowest RMSE.</span>
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Mazwin, Arleena Masngut, Ismail Shuhaida, Mustapha Aida, and Mohd Yasin Suhaila. "Comparison of daily rainfall forecasting using multilayer perceptron neural network model." International Journal of Artificial Intelligence (IJ-AI) 9, no. 3 (2020): 456–63. https://doi.org/10.11591/ijai.v9.i3.pp456-463.
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Rainfall is important in predicting weather forecast particularly to the agriculture sector and also in environment which gives great contribution towards the economy of the nation. Thus, it is important for the hydrologists to forecast daily rainfall in order to help the other people in the agriculture sector to proceed with their harvesting schedules accordingly and to make sure the results of their crops would be satisfying. This study is set to forecast the daily rainfall future value using ARIMA model and Artificial Neural Network (ANN) model. Both method is evaluated by using Mean Absolute Error (MAE), Mean Forecast Error (MFE), Root Mean Squared Error (RMSE) and coefficient of determination (R ). The results showed that ANN model has outperformed results than ARIMA model. The results also showed ANN has under-forecast the daily rainfall data by 2.21% compare to ARIMA with over-forecast of -3.34%. From this study, it shows that the ANN (6,4,1) model produces better results of MAE (8.4208), MFE (2.2188), RMSE (34.6740) and R (0.9432) compared to ARIMA model. This has proved that ANN model has outperformed ARIMA model in predicting daily rainfall values.
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Kolagati, Santosh, Thenuga Priyadharshini, and V. Mary Anita Rajam. "Exposing deepfakes using a deep multilayer perceptron – convolutional neural network model." International Journal of Information Management Data Insights 2, no. 1 (2022): 100054. http://dx.doi.org/10.1016/j.jjimei.2021.100054.
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Arleena Masngut, Mazwin, Shuhaida Ismail, Aida Mustapha, and Suhaila Mohd Yasin. "Comparison of daily rainfall forecasting using multilayer perceptron neural network model." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 3 (2020): 456. http://dx.doi.org/10.11591/ijai.v9.i3.pp456-463.
Full textAbstract:
Rainfall is important in predicting weather forecast particularly to the agriculture sector and also in environment which gives great contribution towards the economy of the nation. Thus, it is important for the hydrologists to forecast daily rainfall in order to help the other people in the agriculture sector to proceed with their harvesting schedules accordingly and to make sure the results of their crops would be satisfying. This study is set to forecast the daily rainfall future value using ARIMA model and Artificial Neural Network (ANN) model. Both method is evaluated by using Mean Absolute Error (MAE), Mean Forecast Error (MFE), Root Mean Squared Error (RMSE) and coefficient of determination (R ). The results showed that ANN model has outperformed results than ARIMA model. The results also showed ANN has under-forecast the daily rainfall data by 2.21% compare to ARIMA with over-forecast of -3.34%. From this study, it shows that the ANN (6,4,1) model produces better results of MAE (8.4208), MFE (2.2188), RMSE (34.6740) and R (0.9432) compared to ARIMA model. This has proved that ANN model has outperformed ARIMA model in predicting daily rainfall values.
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Garcia, Samuel, and Mingjun Zhang. "Higher-order HDL: Applied to MLP neural network hardware implementation." E3S Web of Conferences 631 (2025): 02004. https://doi.org/10.1051/e3sconf/202563102004.
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In this article, we describe a methodology for the rapid implementation of a hardware architecture using a higher-order approach. This methodology uses a combination of TCL and VHDL for higher-order coding (i.e. code produced by code) and is supported by industry-standard HDL development tools. To explore this methodology, we used an FPGA implementation of an artificial neural network (ANN) as a guinea pig application. This enabled us to produce a fully generic multilayer perceptron model where the number of layers, the size of each layer, the types of synaptic signals and the activation function are easily customizable. Not only does this approach make the development of such an application faster, but the high degree of genericity of the model cannot be achieved with conventional VHDL methodology. This article presents feedback from our first steps with this methodology and its application to MLP hardware architecture. Index Terms—VHDL, TCL, Artificial Neural Networks, Multilayer Perceptron, higher-order programming, Methodology
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Ngoc, Tran Thanh, Le Van Dai, and Dang Thi Phuc. "Grid search of multilayer perceptron based on the walk-forward validation methodology." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1742. http://dx.doi.org/10.11591/ijece.v11i2.pp1742-1751.
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Multilayer perceptron neural network is one of the widely used method for load forecasting. There are hyperparameters which can be used to determine the network structure and used to train the multilayer perceptron neural network model. This paper aims to propose a framework for grid search model based on the walk-forward validation methodology. The training process will specify the optimal models which satisfy requirement for minimum of accuracy scores of root mean square error, mean absolute percentage error and mean absolute error. The testing process will evaluate the optimal models along with the other ones. The results indicated that the optimal models have accuracy scores near the minimum values. The US airline passenger and Ho Chi Minh city load demand data were used to verify the accuracy and reliability of the grid search framework.
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Azizi, Aydin, Farshid Entessari, Kambiz Ghaemi Osgouie, and Amirhossein Rezaei Rashnoodi. "Introducing Neural Networks as a Computational Intelligent Technique." Applied Mechanics and Materials 464 (November 2013): 369–74. http://dx.doi.org/10.4028/www.scientific.net/amm.464.369.
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. Neural networks have been applied very successfully in the identification and control of dynamic systems. The universal approximation capabilities of the multilayer perceptron have made it a popular choice for modeling nonlinear systems and for implementing general-purpose nonlinear controllers. In this paper we try to model and control the mass-spring-damper mechanism as a 1 DOF system using neural networks. The control architecture used in this paper is Model reference controller (MRC) as one of the popular neural network control architectures.
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Ismail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi, and A. Abdul-Aziz. "Predicting vehicle parking space availability using multilayer perceptron neural network." IOP Conference Series: Materials Science and Engineering 1176, no. 1 (2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.
Full textAbstract:
Abstract In this study, we have investigated potential use of Multilayer Perceptron (MLP) to predict parking space availability for use within Field Programmable Gate Array (FPGA) accelerated embedded devices. While previous studies have explored the use of MLP for classification problem in FPGA, very little studies concentrated on the potential use of MLP in regression problem, especially in parking space forecasting. Therefore we formulated five Multi-Layer Perceptron (MLP) models with varying hidden units to perform single-step prediction to forecast parking space availability within the next 15 minutes based on the previous one-hour parking occupancy. The proposed models were trained on the historical data of Kuala Lumpur Convention Center dataset and evaluated against baseline ARIMA models. The results have shown that our proposed MLP model performed relatively well against baseline model with the root mean square error between (RMSE) 78.25 to 78.41 and mean absolute error (MAE) between 37.02 to 39.17.
50
Ismail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi, and A. Abdul-Aziz. "Predicting vehicle parking space availability using multilayer perceptron neural network." IOP Conference Series: Materials Science and Engineering 1176, no. 1 (2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.
Full textAbstract:
Abstract In this study, we have investigated potential use of Multilayer Perceptron (MLP) to predict parking space availability for use within Field Programmable Gate Array (FPGA) accelerated embedded devices. While previous studies have explored the use of MLP for classification problem in FPGA, very little studies concentrated on the potential use of MLP in regression problem, especially in parking space forecasting. Therefore we formulated five Multi-Layer Perceptron (MLP) models with varying hidden units to perform single-step prediction to forecast parking space availability within the next 15 minutes based on the previous one-hour parking occupancy. The proposed models were trained on the historical data of Kuala Lumpur Convention Center dataset and evaluated against baseline ARIMA models. The results have shown that our proposed MLP model performed relatively well against baseline model with the root mean square error between (RMSE) 78.25 to 78.41 and mean absolute error (MAE) between 37.02 to 39.17.