Journal articles: 'Multilayer perceptron network'

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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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KUKOLJ, DRAGAN D., MIROSLAVA T. BERKO-PUSIC, and BRANISLAV ATLAGIC. "Experimental design of supervisory control functions based on multilayer perceptrons." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 5 (2001): 425–31. http://dx.doi.org/10.1017/s0890060401155058.

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This article presents the results of research concerning possibilities of applying multilayer perceptron type of neural network for fault diagnosis, state estimation, and prediction in the gas pipeline transmission network. The influence of several factors on accuracy of the multilayer perceptron was considered. The emphasis was put on the multilayer perceptrons' function as a state estimator. The choice of the most informative features, the amount and sampling period of training data sets, as well as different configurations of multilayer perceptrons were analyzed.

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Ringienė, Laura, and Gintautas Dzemyda. "Specialios struktūros daugiasluoksnis perceptronas daugiamačiams duomenims vizualizuoti." Informacijos mokslai 50 (January 1, 2009): 358–64. http://dx.doi.org/10.15388/im.2009.0.3210.

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Pasiūlytas ir ištirtas radialinių bazinių funkcijų ir daugiasluoksnio perceptrono junginys daugiamačiams duomenis vizualizuoti. Siūlomas vizualizavimo būdas apima daugiamačių duomenų matmenų mažinimą naudojant radialines bazines funkcijas, daugiamačių duomenų suskirstymą į klasterius, klasterį charakterizuojančių skaitinių reikšmių nustatymą ir daugiamačių duomenų vizualizavimą dirbtinio neuroninio tinklo paskutiniame paslėptajame sluoksnyje.Special Multilayer Perceptron for Multidimensional Data VisualizationLaura Ringienė, Gintautas Dzemyda SummaryIn this paper a special feed forward neural network, consisting of the radial basis function layer and a multilayer perceptron is presented. The multilayer perceptron has been proposed and investigated for multidimensional data visualization. The roposedvisualization approach includes data clustering, determining the parameters of the radial basis function and forming the data set to train the multilayer perceptron. The outputs of the last hidden layer are assigned as coordinates of the visualized points.

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Araujo, P., G. Astray, J. A. Ferrerio-Lage, J. C. Mejuto, J. A. Rodriguez-Suarez, and B. Soto. "Multilayer perceptron neural network for flow prediction." J. Environ. Monit. 13, no. 1 (2011): 35–41. http://dx.doi.org/10.1039/c0em00478b.

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ANDERSEN, TIMOTHY L., and TONY R. MARTINEZ. "DMP3: A DYNAMIC MULTILAYER PERCEPTRON CONSTRUCTION ALGORITHM." International Journal of Neural Systems 11, no. 02 (2001): 145–65. http://dx.doi.org/10.1142/s0129065701000576.

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This paper presents DMP3 (Dynamic Multilayer Perceptron 3), a multilayer perceptron (MLP) constructive training method that constructs MLPs by incrementally adding network elements of varying complexity to the network. DMP3 differs from other MLP construction techniques in several important ways, and the motivation for these differences are given. Information gain rather than error minimization is used to guide the growth of the network, which increases the utility of newly added network elements and decreases the likelihood that a premature dead end in the growth of the network will occur. The generalization performance of DMP3 is compared with that of several other well-known machine learning and neural network learning algorithms on nine real world data sets. Simulation results show that DMP3 performs better (on average) than any of the other algorithms on the data sets tested. The main reasons for this result are discussed in detail.

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Kumar, Deepak. "Power System Restoration Using Multilayer Perceptron." International Journal of Engineering, Science and Information Technology 1, no. 1 (2021): 10–14. http://dx.doi.org/10.52088/ijesty.v1i1.35.

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In recent years, power systems are being operated nearer to their limits due to economic competition and deregulation. Also, nowadays the challenge is to include large and ever increasing amounts of decentralized generated power into the existing transmission network and at the same time comply with the electricity market transmission demands. Both factors increase the risk of blackout. After which, power needs to be restored as quickly and reliably as possible and, accordingly, detailed power system restoration plans are required. The multilayer perceptron network is chosen for a more precise examination.

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Kaur, Jatinder, Dr Mandeep Singh, Pardeep Singh Bains, and Gagandeep Singh. "Analysis of Multi layer Perceptron Network." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 2 (2013): 600–606. http://dx.doi.org/10.24297/ijct.v7i2.3462.

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In this paper, we introduce the multilayer Perceptron (feedforward) neural network (MLPs) and used it for a function approximation. For the training of MLP, we have used back propagation algorithm principle. The main purpose of this paper lies in changing the number of hidden layers of MLP for achieving minimum value of mean square error.

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Kusumoputro, Benyamin, and Teguh P. Arsyad. "Recognizing Odor Mixtures Using Optimized Fuzzy Neural Network Through Genetic Algorithms." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 3 (2005): 290–96. http://dx.doi.org/10.20965/jaciii.2005.p0290.

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Recognizing odor mixtures is rather difficult in artificial odor recognition system, especially when the number of sensors is limited. Classification is further hampered if the number of unlearned odor mixtures classes is increased. We developed a fuzzy-neuro multilayer perceptron as a pattern classifier and compared its recognition with that of the Probabilistic Neural Network and Back-propagation Neural Network. To enhance the recognition capability of the system, we then optimized fuzzy-neuro multilayer perceptron topology by deleting its weak weight connections using Genetic Algorithms. Experimental results show that the optimized fuzzy-neuro multilayer perceptron has the highest recognition in 18 classes of two-mixture odors with almost 98.2% when using hardware with 16 sensors, compared to 83.3% when using 8 sensors.

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talal gadawe, Nour, and Rafid Ahmed Khalil. "FPGA Implementation of a Multilayer Perceptron (MLP) Network." AL-Rafdain Engineering Journal (AREJ) 17, no. 1 (2009): 1–13. http://dx.doi.org/10.33899/rengj.2009.38557.

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LERNER, B., H. GUTERMAN, I. DINSTEIN, and Y. ROMEM. "HUMAN CHROMOSOME CLASSIFICATION USING MULTILAYER PERCEPTRON NEURAL NETWORK." International Journal of Neural Systems 06, no. 03 (1995): 359–70. http://dx.doi.org/10.1142/s012906579500024x.

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A multilayer perceptron (MLP) neural network (NN) has been studied for human chromosome classification. Only 10–20 examples were required for the MLP NN to reach its ultimate performance classifying chromosomes of 5 types. The empirical dependence of the entropic error on the number of examples was found to be highly comparable to the 1/t function. The principal component analysis (PCA) was used, both for network initialization and for feature reduction purposes. The PCA demonstrated the importance of retaining most of the image information whenever small training sets are used. The MLP NN classifier outperformed the Bayes piecewise classifier for all the cases tested. The MLP classifier was found to be almost unsusceptible to the ratio of the number of training vectors to the number of features, whereas the piecewise classifier was highly dependent on this ratio.

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Hans, Kanchan, Laxmi Ahuja, and S. K. Muttoo. "Detecting redirection spam using multilayer perceptron neural network." Soft Computing 21, no. 13 (2017): 3803–14. http://dx.doi.org/10.1007/s00500-017-2531-9.

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Sarkar, Arindam. "Multilayer neural network synchronized secured session key based encryption in wireless communication." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 1 (2019): 44. http://dx.doi.org/10.11591/ijai.v8.i1.pp44-53.

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In this paper, multilayer neural network synchronized session key based encryption has been proposed for wireless communication of data/information. Multilayer perceptron transmitting systems at both ends accept an identical input vector, generate an output bit and the network are trained based on the output bit which is used to form a protected variable length secret-key. For each session, different hidden layer of multilayer neural network is selected randomly and weights or hidden units of this selected hidden layer help to form a secret session key. The plain text is encrypted through chaining , cascaded xoring of multilayer perceptron generated session key. If size of the final block of plain text is less than the size of the key then this block is kept unaltered. Receiver will use identical multilayer perceptron generated session key for performing deciphering process for getting the plain text. Parametric tests have been done and results are compared in terms of Chi-Square test, response time in transmission with some existing classical techniques, which shows comparable results for the proposed technique.

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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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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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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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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.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.

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LEHTOKANGAS, MIKKO. "FAST LEARNING USING MULTILAYER PERCEPTRON NETWORKS WITH ADAPTIVE CENTROID LAYER." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 02 (2000): 211–23. http://dx.doi.org/10.1142/s0218001400000143.

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A hybrid neural network architecture is investigated for classification purposes. The proposed hybrid is based on the multilayer perceptron (MLP) network. In addition to the usual hidden layers the first hidden layer is selected to be an adaptive centroid layer. Each unit in this new layer incorporates a centroid vector that is located somewhere in the space spanned by the input variables. The output of these units is the Euclidean distance between the centroid vector and the inputs. The centroid layer has some resemblance to the hidden layer of the radial basis function (RBF) networks. Therefore the proposed design can be regarded as a sort of hybrid of the MLP and RBF networks. The presented benchmark experiments demonstrate that the proposed hybrid can provide significant advantages over standard MLPs in terms of fast and efficient learning, and compact network structure.

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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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QUEK, SIOW SAN, CHEE PENG LIM, and KOK KHIANG PEH. "PREDICTION OF DRUG DISSOLUTION PROFILES USING ARTIFICIAL NEURAL NETWORKS." International Journal of Computational Intelligence and Applications 01, no. 02 (2001): 187–202. http://dx.doi.org/10.1142/s1469026801000214.

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This paper investigates the efficacy and reliability of Artificial Neural Networks (ANNs) as an intelligent decision support tool for pharmaceutical product formulation. Two case studies have been employed to evaluate capabilities of the Multilayer Perceptron network in predicting drug dissolution/release profiles. Performances of the network were evaluated using similarity factor (f2) — an index recommended by the United States Food and Drug Administration for profile comparison in pharmaceutical research. In addition, the bootstrap method was applied to assess the network prediction reliability by estimating confidence intervals associated with the results. The Multilayer Perceptron network also demonstrated a superior performance in comparison with multiple regression models. The results reveal that the ANN system has potentials to be a decision support tool for profile prediction in pharmaceutical experimentation, and the bootstrap method could be used as a means to assess reliability of the network prediction.

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Dawson, C. W., C. Harpham, R. L. Wilby, and Y. Chen. "Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China." Hydrology and Earth System Sciences 6, no. 4 (2002): 619–26. http://dx.doi.org/10.5194/hess-6-619-2002.

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Abstract. While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP), and the radial basis function network (RBF). Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam) for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting

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Sarkar, Arindam, Joydeep Dey, and Anirban Bhowmik. "Multilayer neural network synchronized secured session key based encryption in wireless communication." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (2019): 169. http://dx.doi.org/10.11591/ijeecs.v14.i1.pp169-177.

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Energy computation concept of multilayer neural network synchronized on derived transmission key based encryption system has been proposed for wireless transactions. Multilayer perceptron transmitting machines accepted same input array, which in turn generate a resultant bit and the networks were trained accordingly to form a protected variable length secret-key. For each session, different hidden layer of multilayer neural network is selected randomly and weights of hidden units of this selected hidden layer help to form a secret session key. A novel approach to generate a transmission key has been explained in this proposed methodology. The last thirty two bits of the session key were taken into consideration to construct the transmission key. Inverse operations were carried out by the destination perceptron to decipher the data. Floating frequency analysis of the proposed encrypted stream of bits has yielded better degree of security results. Energy computation of the processed nodes inside multi layered networks can be done using this proposed frame of work.

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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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Majidzadeh Gorjani, Ojan, Radek Byrtus, Jakub Dohnal, Petr Bilik, Jiri Koziorek, and Radek Martinek. "Human Activity Classification Using Multilayer Perceptron." Sensors 21, no. 18 (2021): 6207. http://dx.doi.org/10.3390/s21186207.

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The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and fall detection. This work uses a multilayer perceptron neural network to recognize multiple human activities from wrist- and ankle-worn devices. The developed models show very high recognition accuracy across all activity classes. The cross-validation results indicate accuracy levels above 98% across all models, and scoring evaluation methods only resulted in an average accuracy reduction of 10%.

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S.Saber, Amany, and Mohamed A. El-Rashidy. "An Effective Intelligent Self-Construction Multilayer Perceptron Neural Network." International Journal of Computer Applications 98, no. 11 (2014): 23–28. http://dx.doi.org/10.5120/17228-7552.

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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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Velasco, Lemuel Clark P., Ruth P. Serquiña, Mohammad Shahin A. Abdul Zamad, Bryan F. Juanico, and Junneil C. Lomocso. "Week-ahead Rainfall Forecasting Using Multilayer Perceptron Neural Network." Procedia Computer Science 161 (2019): 386–97. http://dx.doi.org/10.1016/j.procs.2019.11.137.

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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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Luo, Rui. "Optical proximity correction using a multilayer perceptron neural network." Journal of Optics 15, no. 7 (2013): 075708. http://dx.doi.org/10.1088/2040-8978/15/7/075708.

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Pereira Härter, Fabrício, and Haroldo Fraga de Campos Velho. "Multilayer Perceptron Neural Network in a Data Assimilation Scenario." Engineering Applications of Computational Fluid Mechanics 4, no. 2 (2010): 237–45. http://dx.doi.org/10.1080/19942060.2010.11015313.

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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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Tang, Xiao, Lei Zhang, and Xiaoli Ding. "SAR image despeckling with a multilayer perceptron neural network." International Journal of Digital Earth 12, no. 3 (2018): 354–74. http://dx.doi.org/10.1080/17538947.2018.1447032.

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Mat Isa, Nor Ashidi, and Wan Mohd Fahmi Wan Mamat. "Clustered-Hybrid Multilayer Perceptron network for pattern recognition application." Applied Soft Computing 11, no. 1 (2011): 1457–66. http://dx.doi.org/10.1016/j.asoc.2010.04.017.

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Bishop, Chris. "A FAST PROCEDURE FOR RETRAINING THE MULTILAYER PERCEPTRON." International Journal of Neural Systems 02, no. 03 (1991): 229–36. http://dx.doi.org/10.1142/s0129065791000212.

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In this paper we describe a fast procedure for retraining a feedforward network, previously trained by error backpropagation, following a small change in the training data. This technique would permit fine calibration of individual neural network based control systems in a mass-production environment. We also derive a generalised error backpropagation algorithm which allows an exact evaluation of all of the terms in the Hessian matrix. The fast retraining procedure is illustrated using a simple example.

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García Fernández, F., L. García Esteban, P. de Palacios, A. García-Iruela, and R. Cabedo Gallén. "Estimating the Uncertainty of a Multilayer Perceptron Using the Monte Carlo Method." Advanced Materials Research 628 (December 2012): 324–29. http://dx.doi.org/10.4028/www.scientific.net/amr.628.324.

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Artificial neural networks have become a powerful modeling tool. However, although they obtain an output with very good accuracy, they provide no information about the uncertainty of the network or its coverage intervals. This study describes the application of the Monte Carlo method to obtain the output uncertainty and coverage intervals of a particular type of artificial neural network: the multilayer perceptron.

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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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Omar, Raid Adnan. "Classification of brain tumors using the multilayer perceptron artificial neural network." Iraqi Journal of Physics (IJP) 16, no. 36 (2018): 190–98. http://dx.doi.org/10.30723/ijp.v16i36.43.

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Information from 54 Magnetic Resonance Imaging (MRI) brain tumor images (27 benign and 27 malignant) were collected and subjected to multilayer perceptron artificial neural network available on the well know software of IBM SPSS 17 (Statistical Package for the Social Sciences). After many attempts, automatic architecture was decided to be adopted in this research work. Thirteen shape and statistical characteristics of images were considered. The neural network revealed an 89.1 % of correct classification for the training sample and 100 % of correct classification for the test sample. The normalized importance of the considered characteristics showed that kurtosis accounted for 100 % which means that this variable has a substantial effect on how the network perform when predicting cases of brain tumor, contrast accounted for 64.3 %, correlation accounted for 56.7 %, and entropy accounted for 54.8 %. All remaining characteristics accounted for 21.3-46.8 % of normalized importance. The output of the neural networks showed that sensitivity and specificity were scored remarkably high level of probability as it approached % 96.

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HANDZEL, AMIR A., T. GROSSMAN, E. DOMANY, S. TAREM, and E. DUCHOVNI. "A NEURAL NETWORK CLASSIFIER IN EXPERIMENTAL PARTICLE PHYSICS." International Journal of Neural Systems 04, no. 02 (1993): 95–108. http://dx.doi.org/10.1142/s0129065793000109.

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A classification problem in high energy physics has been solved on simulated data using a simple multilayer perceptron comprising binary units which was trained with the CHIR algorithm. The unstable training of such a network on a nonseparable set has been overcome by selecting those weight vectors with good performance while providing a flexible choice of the two types of classification errors. Specific features of the problem have been exploited in order to simplify and optimize the solution which has been compared to the popular backpropagation algorithm and found to perform on a similar level. Additional aspects of this work are the use of the CHIR algorithm on continuous input and incorporating the classic idea of a Φ-machine in a multilayer perceptron.

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Bishop, Chris. "Exact Calculation of the Hessian Matrix for the Multilayer Perceptron." Neural Computation 4, no. 4 (1992): 494–501. http://dx.doi.org/10.1162/neco.1992.4.4.494.

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The elements of the Hessian matrix consist of the second derivatives of the error measure with respect to the weights and thresholds in the network. They are needed in Bayesian estimation of network regularization parameters, for estimation of error bars on the network outputs, for network pruning algorithms, and for fast retraining of the network following a small change in the training data. In this paper we present an extended backpropagation algorithm that allows all elements of the Hessian matrix to be evaluated exactly for a feedforward network of arbitrary topology. Software implementation of the algorithm is straightforward.

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Al-batah, Mohammad Subhi, Mutasem Sh Alkhasawneh, Lea Tien Tay, Umi Kalthum Ngah, Habibah Hj Lateh, and Nor Ashidi Mat Isa. "Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/512158.

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Landslides are one of the dangerous natural phenomena that hinder the development in Penang Island, Malaysia. Therefore, finding the reliable method to predict the occurrence of landslides is still the research of interest. In this paper, two models of artificial neural network, namely, Multilayer Perceptron (MLP) and Cascade Forward Neural Network (CFNN), are introduced to predict the landslide hazard map of Penang Island. These two models were tested and compared using eleven machine learning algorithms, that is, Levenberg Marquardt, Broyden Fletcher Goldfarb, Resilient Back Propagation, Scaled Conjugate Gradient, Conjugate Gradient with Beale, Conjugate Gradient with Fletcher Reeves updates, Conjugate Gradient with Polakribiere updates, One Step Secant, Gradient Descent, Gradient Descent with Momentum and Adaptive Learning Rate, and Gradient Descent with Momentum algorithm. Often, the performance of the landslide prediction depends on the input factors beside the prediction method. In this research work, 14 input factors were used. The prediction accuracies of networks were verified using the Area under the Curve method for the Receiver Operating Characteristics. The results indicated that the best prediction accuracy of 82.89% was achieved using the CFNN network with the Levenberg Marquardt learning algorithm for the training data set and 81.62% for the testing data set.

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MARTIN, PHILIPPE, and CAMILLE BELLISSANT. "NEURAL NETWORKS FOR THE RECOGNITION OF ENGRAVED MUSICAL SCORES." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 01 (1992): 193–208. http://dx.doi.org/10.1142/s0218001492000114.

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The image analysis levels of a recognition system for engraved musical scores are described. Recognizing musical score images requires an accurate segmentation stage to isolate symbols from staff lines. This symbols/staves segregation is achieved by the use of inscribed line (chord) information. This information, processed by a multilayer perceptron, allows an efficient segmentation in terms of the remaining connected components. Some of these components are then classified, using another network, according to a coding of their skeleton graph. Special attention is paid to the design of the networks: the architectures are adapted to the specificities of each task. Multilayer perceptrons are employed here together with other more classical image analysis techniques which are also presented.

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Özkan, Ali Osman. "Effect of Normalization Techniques on Multilayer Perceptron Neural Network Classification Performance for Rheumatoid Arthritis Disease Diagnosis." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (2017): 733–39. http://dx.doi.org/10.31142/ijtsrd3576.

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Al-Khasawneh, Ahmad. "Diagnosis of Breast Cancer Using Intelligent Information Systems Techniques." International Journal of E-Health and Medical Communications 7, no. 1 (2016): 65–75. http://dx.doi.org/10.4018/ijehmc.2016010104.

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Breast cancer is the second leading cause of cancer deaths in women worldwide. Early diagnosis of this illness can increase the chances of long-term survival of cancerous patients. To help in this aid, computerized breast cancer diagnosis systems are being developed. Machine learning algorithms and data mining techniques play a central role in the diagnosis. This paper describes neural network based approaches to breast cancer diagnosis. The aim of this research is to investigate and compare the performance of supervised and unsupervised neural networks in diagnosing breast cancer. A multilayer perceptron has been implemented as a supervised neural network and a self-organizing map as an unsupervised one. Both models were simulated using a variety of parameters and tested using several combinations of those parameters in independent experiments. It was concluded that the multilayer perceptron neural network outperforms Kohonen's self-organizing maps in diagnosing breast cancer even with small data sets.

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Zeng, Xiaoqin, and Daniel S. Yeung. "A Quantified Sensitivity Measure for Multilayer Perceptron to Input Perturbation." Neural Computation 15, no. 1 (2003): 183–212. http://dx.doi.org/10.1162/089976603321043757.

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The sensitivity of a neural network's output to its input perturbation is an important issue with both theoretical and practical values. In this article, we propose an approach to quantify the sensitivity of the most popular and general feedforward network: multilayer perceptron (MLP). The sensitivity measure is defined as the mathematical expectation of output deviation due to expected input deviation with respect to overall input patterns in a continuous interval. Based on the structural characteristics of the MLP, a bottom-up approach is adopted. A single neuron is considered first, and algorithms with approximately derived analytical expressions that are functions of expected input deviation are given for the computation of its sensitivity. Then another algorithm is given to compute the sensitivity of the entire MLP network. Computer simulations are used to verify the derived theoretical formulas. The agreement between theoretical and experimental results is quite good. The sensitivity measure can be used to evaluate the MLP's performance.

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Verma, Pratibha, Vineet Kumar Awasthi, and Sanat Kumar Sahu. "Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network." International Journal of Applied Evolutionary Computation 12, no. 3 (2021): 35–43. http://dx.doi.org/10.4018/ijaec.2021070103.

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Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neural network (ANN) method for the classification of the CAD dataset. Therefore, the ten-fold cross-validation (T-FCV) method, P-MLP algorithms, and base classifiers of MLP were employed. The P-MLP algorithm yielded very high accuracy (86.47% in CAD-56 and 98.35% in CAD-59 datasets) and F1-Score (90.36% in CAD-56 and 98.83% in CAD-59 datasets) rates, which have not been reported simultaneously in the MLP.

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Berkane, Mohamed, Kenza Belhouchette, and Hacene Belhadef. "Emotion Recognition Approach Using Multilayer Perceptron Network and Motion Estimation." International Journal of Synthetic Emotions 10, no. 1 (2019): 38–53. http://dx.doi.org/10.4018/ijse.2019010102.

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Man-machine interaction is an interdisciplinary field of research that provides natural and multimodal ways of interaction between humans and computers. For this purpose, the computer must understand the emotional state of the person with whom it interacts. This article proposes a novel method for detecting and classify the basic emotions like sadness, joy, anger, fear, disgust, surprise, and interest that was introduced in previous works. As with all emotion recognition systems, the approach follows the basic steps, such as: facial detection and facial feature extraction. In these steps, the contribution is expressed by using strategic face points and interprets motions as action units extracted by the FACS system. The second contribution is at the level of the classification step, where two classifiers were used: Kohonen self-organizing maps (KSOM) and multilayer perceptron (MLP) in order to obtain the best results. The obtained results show that the recognition rate of basic emotions has improved, and the running time was minimized by reducing resource use.

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Adigun, Taiwo, and Angela Makolo. "Multilayer Perceptron based Model of Large-Scale Gene Regulatory Network." International Journal of Computer Applications 178, no. 42 (2019): 6–15. http://dx.doi.org/10.5120/ijca2019919148.

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Heidari, Ali Asghar, Hossam Faris, Ibrahim Aljarah, and Seyedali Mirjalili. "An efficient hybrid multilayer perceptron neural network with grasshopper optimization." Soft Computing 23, no. 17 (2018): 7941–58. http://dx.doi.org/10.1007/s00500-018-3424-2.

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Chung-Hsien Wu, Jhing-Fa Wang, and Wen-Horng Wu. "A shunting multilayer perceptron network for confusing/composite pattern recognition." Pattern Recognition 24, no. 11 (1991): 1093–103. http://dx.doi.org/10.1016/0031-3203(91)90124-n.

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Wiegerinck, Wim, and Tom Heskes. "How Dependencies between Successive Examples Affect On-Line Learning." Neural Computation 8, no. 8 (1996): 1743–65. http://dx.doi.org/10.1162/neco.1996.8.8.1743.

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We study the dynamics of on-line learning for a large class of neural networks and learning rules, including backpropagation for multilayer perceptrons. In this paper, we focus on the case where successive examples are dependent, and we analyze how these dependencies affect the learning process. We define the representation error and the prediction error. The representation error measures how well the environment is represented by the network after learning. The prediction error is the average error that a continually learning network makes on the next example. In the neighborhood of a local minimum of the error surface, we calculate these errors. We find that the more predictable the example presentation, the higher the representation error, i.e., the less accurate the asymptotic representation of the whole environment. Furthermore we study the learning process in the presence of a plateau. Plateaus are flat spots on the error surface, which can severely slow down the learning process. In particular, they are notorious in applications with multilayer perceptrons. Our results, which are confirmed by simulations of a multilayer perceptron learning a chaotic time series using backpropagation, explain how dependencies between examples can help the learning process to escape from a plateau.

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Back, Andrew D., and Ah Chung Tsoi. "An Adaptive Lattice Architecture for Dynamic Multilayer Perceptrons." Neural Computation 4, no. 6 (1992): 922–31. http://dx.doi.org/10.1162/neco.1992.4.6.922.

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Time-series modeling is a topic of growing interest in neural network research. Various methods have been proposed for extending the nonlinear approximation capabilities to time-series modeling problems. A multilayer perceptron (MLP) with a global-feedforward local-recurrent structure was recently introduced as a new approach to modeling dynamic systems. The network uses adaptive infinite impulse response (IIR) synapses (it is thus termed an IIR MLP), and was shown to have good modeling performance. One problem with linear IIR filters is that the rate of convergence depends on the covariance matrix of the input data. This extends to the IIR MLP: it learns well for white input signals, but converges more slowly with nonwhite inputs. To solve this problem, the adaptive lattice multilayer perceptron (AL MLP), is introduced. The network structure performs Gram-Schmidt orthogonalization on the input data to each synapse. The method is based on the same principles as the Gram-Schmidt neural net proposed by Orfanidis (1990b), but instead of using a network layer for the orthogonalization, each synapse comprises an adaptive lattice filter. A learning algorithm is derived for the network that minimizes a mean square error criterion. Simulations are presented to show that the network architecture significantly improves the learning rate when correlated input signals are present.

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Odeh, Ammar Jamil, Ismail Keshta, and Eman Abdelfattah. "Efficient Detection of Phishing Websites Using Multilayer Perceptron." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 11 (2020): 22. http://dx.doi.org/10.3991/ijim.v14i11.13903.

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Phishing is a type of Internet fraud that aims to acquire the credential of users via scamming websites. In this paper, a novel approach is utilized that uses a Neural Network with a multilayer perceptron to detect the scam URL. The proposed system improves the accuracy of the scam detection system as it achieves a high accuracy percentage of 98.5%.

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Journal articles on the topic 'Multilayer perceptron network'

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