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Artykuły w czasopismach na temat „Multilayer Perceptron neural network model”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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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

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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

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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 itsel

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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

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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

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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 Pre

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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 m

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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

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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

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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 screeni

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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 d

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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 th

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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 pe

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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 e

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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 incl

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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

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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

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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

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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

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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

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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 counse

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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 arti

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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 de

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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 l

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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 (h

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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.

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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 (h

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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

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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,

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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 pape

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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 te

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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. T

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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) a

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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

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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 o

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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 sp

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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.

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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 sp

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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’

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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 percept

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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 effici

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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 investme

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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.

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<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

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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 Absolu

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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.

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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 Absolu

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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 funct

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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

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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.

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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 ne

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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.

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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 ne

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