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Journal articles on the topic 'Multi-layer perceptron networks (MLPNs)'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Przybył, Krzysztof, Krzysztof Koszela, Franciszek Adamski, Katarzyna Samborska, Katarzyna Walkowiak, and Mariusz Polarczyk. "Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders." Sensors 21, no. 17 (2021): 5823. http://dx.doi.org/10.3390/s21175823.

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In the paper, an attempt was made to use methods of artificial neural networks (ANN) and Fourier transform infrared spectroscopy (FTIR) to identify raspberry powders that are different from each other in terms of the amount and the type of polysaccharide. Spectra in the absorbance function (FTIR) were prepared as well as training sets, taking into account the structure of microparticles acquired from microscopic images with Scanning Electron Microscopy (SEM). In addition to the above, Multi-Layer Perceptron Networks (MLPNs) with a set of texture descriptors (machine learning) and Convolution N

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Rohman, Budiman Putra Asmaur, and Dayat Kurniawan. "Classification of Radar Environment Using Ensemble Neural Network with Variation of Hidden Neuron Number." Jurnal Elektronika dan Telekomunikasi 17, no. 1 (2017): 19. http://dx.doi.org/10.14203/jet.v17.19-24.

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Target detection is a mandatory task of radar system so that the radar system performance is mainly determined by its detection rate. Constant False Alarm Rate (CFAR) is a detection algorithm commonly used in radar systems. This method is divided into several approaches which have different performance in the different environments. Therefore, this paper proposes an ensemble neural network based classifier with a variation of hidden neuron number for classifying the radar environments. The result of this research will support the improvement of the performance of the target detection on the ra

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Bologna, Guido. "A Simple Convolutional Neural Network with Rule Extraction." Applied Sciences 9, no. 12 (2019): 2411. http://dx.doi.org/10.3390/app9122411.

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Classification responses provided by Multi Layer Perceptrons (MLPs) can be explained by means of propositional rules. So far, many rule extraction techniques have been proposed for shallow MLPs, but not for Convolutional Neural Networks (CNNs). To fill this gap, this work presents a new rule extraction method applied to a typical CNN architecture used in Sentiment Analysis (SA). We focus on the textual data on which the CNN is trained with “tweets” of movie reviews. Its architecture includes an input layer representing words by “word embeddings”, a convolutional layer, a max-pooling layer, fol

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CAIRNS, GRAHAM, and LIONEL TARASSENKO. "PERTURBATION TECHNIQUES FOR ON-CHIP LEARNING WITH ANALOGUE VLSI MLPs." Journal of Circuits, Systems and Computers 06, no. 02 (1996): 93–113. http://dx.doi.org/10.1142/s0218126696000108.

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Microelectronic neural network technology has become sufficiently mature over the past few years that reliable performance can now be obtained from VLSI circuits under carefully controlled conditions (see Refs. 8 or 13 for example). The use of analogue VLSI allows low power, area efficient hardware realisations which can perform the computationally intensive feed-forward operation of neural networks at high speed, making real-time applications possible. In this paper we focus on important issues for the successful operation and implementation of on-chip learning with such analogue VLSI neural

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Suprapto, Suprapto, and Edy Riyanto. "Grape Drying Process Using Machine Vision Based on Multilayer Perceptron Networks." Indonesian Journal of Science and Technology 5, no. 3 (2020): 382–94. http://dx.doi.org/10.17509/ijost.v5i3.24991.

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This paper proposed a grape drying machine using computer vision and Multi-layer Perceptron (MLP) method. Computer vision is for taking grapes’ image on conveyor, whereas MLP is for controlling grape drying machine and classifying its output. To evaluate the proposed, a kind of grapes are put on conveyor of the machine and their images are taken every two min. Some parameters of MLP to control the drying machine includes dried grape, temperature, grape area, motor position, and motion speed. Those parameters are to adjust an appropriate MLP’s output, including motion control and heater control

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Geng, Chao, Qingji Sun, and Shigetoshi Nakatake. "Implementation of Analog Perceptron as an Essential Element of Configurable Neural Networks." Sensors 20, no. 15 (2020): 4222. http://dx.doi.org/10.3390/s20154222.

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Perceptron is an essential element in neural network (NN)-based machine learning, however, the effectiveness of various implementations by circuits is rarely demonstrated from chip testing. This paper presents the measured silicon results for the analog perceptron circuits fabricated in a 0.6 μm/±2.5 V complementary metal oxide semiconductor (CMOS) process, which are comprised of digital-to-analog converter (DAC)-based multipliers and phase shifters. The results from the measurement convinces us that our implementation attains the correct function and good performance. Furthermore, we propose

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Bensaoucha, Saddam, Youcef Brik, Sandrine Moreau, Sid Ahmed Bessedik, and Aissa Ameur. "Induction machine stator short-circuit fault detection using support vector machine." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 40, no. 3 (2021): 373–89. http://dx.doi.org/10.1108/compel-06-2020-0208.

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Purpose This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases. Design/methodology/approach To evaluate the performance of the SVM, three supervised algorithms of machine learning, na

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Loukeris, Nikolaos, and Iordanis Eleftheriadis. "Further Higher Moments in Portfolio Selection andA PrioriDetection of Bankruptcy, Under Multi-layer Perceptron Neural Networks, Hybrid Neuro-genetic MLPs, and the Voted Perceptron." International Journal of Finance & Economics 20, no. 4 (2015): 341–61. http://dx.doi.org/10.1002/ijfe.1521.

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Przybył, Krzysztof, Jolanta Wawrzyniak, Krzysztof Koszela, Franciszek Adamski, and Marzena Gawrysiak-Witulska. "Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed." Sensors 20, no. 24 (2020): 7305. http://dx.doi.org/10.3390/s20247305.

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This paper endeavors to evaluate rapeseed samples obtained in the process of storage experiments with different humidity (12% and 16% seed moisture content) and temperature conditions (25 and 30 °C). The samples were characterized by different levels of contamination with filamentous fungi. In order to acquire graphic data, the analysis of the morphological structure of rapeseeds was carried out with the use of microscopy. The acquired database was prepared in order to build up training, validation, and test sets. The process of generating a neural model was based on Convolutional Neural Netwo

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He, Hao, Jiaxiang Zhao, and Guiling Sun. "Prediction of MoRFs in Protein Sequences with MLPs Based on Sequence Properties and Evolution Information." Entropy 21, no. 7 (2019): 635. http://dx.doi.org/10.3390/e21070635.

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Molecular recognition features (MoRFs) are one important type of intrinsically disordered proteins functional regions that can undergo a disorder-to-order transition through binding to their interaction partners. Prediction of MoRFs is crucial, as the functions of MoRFs are associated with many diseases and can therefore become the potential drug targets. In this paper, a method of predicting MoRFs is developed based on the sequence properties and evolutionary information. To this end, we design two distinct multi-layer perceptron (MLP) neural networks and present a procedure to train them. We

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Gierz, Łukasz, Krzysztof Przybył, Krzysztof Koszela, Adamina Duda, and Witold Ostrowicz. "The Use of Image Analysis to Detect Seed Contamination—A Case Study of Triticale." Sensors 21, no. 1 (2020): 151. http://dx.doi.org/10.3390/s21010151.

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Samples of triticale seeds of various qualities were assessed in the study. The seeds were obtained during experiments, reflecting the actual sowing conditions. The experiments were conducted on an original test facility designed by the authors of this study. The speed of the air (15, 20, 25 m/s) transporting seeds in the pneumatic conduit was adjusted to sowing. The resulting graphic database enabled the distinction of six classes of seeds according to their quality and sowing speed. The database was prepared to build training, validation and test sets. The neural model generation process was

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Arshad, R. Rezaei, Gh Sayyad, M. Mosaddeghi, and B. Gharabaghi. "Predicting Saturated Hydraulic Conductivity by Artificial Intelligence and Regression Models." ISRN Soil Science 2013 (June 11, 2013): 1–8. http://dx.doi.org/10.1155/2013/308159.

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Saturated hydraulic conductivity (Ks), among other soil hydraulic properties, is important and necessary in water and mass transport models and irrigation and drainage studies. Although this property can be measured directly, its measurement is difficult and very variable in space and time. Thus pedotransfer functions (PTFs) provide an alternative way to predict the Ks from easily available soil data. This study was done to predict the Ks in Khuzestan province, southwest Iran. Three Intelligence models including (radial basis function neural networks (RBFNN), multi layer perceptron neural netw

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Chen, Can, Luca Zanotti Fragonara, and Antonios Tsourdos. "Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions." Applied Sciences 10, no. 7 (2020): 2391. http://dx.doi.org/10.3390/app10072391.

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In order to achieve a better performance for point cloud analysis, many researchers apply deep neural networks using stacked Multi-Layer-Perceptron (MLP) convolutions over an irregular point cloud. However, applying these dense MLP convolutions over a large amount of points (e.g., autonomous driving application) leads to limitations due to the computation and memory capabilities. To achieve higher performances but decrease the computational complexity, we propose a deep-wide neural network, named ShufflePointNet, which can exploit fine-grained local features, but also reduce redundancies using

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Kim, Taehwan, and Tülay Adalı. "Approximation by Fully Complex Multilayer Perceptrons." Neural Computation 15, no. 7 (2003): 1641–66. http://dx.doi.org/10.1162/089976603321891846.

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We investigate the approximation ability of a multi layer perceptron (MLP) network when it is extended to the complex domain. The main challenge for processing complex data with neural networks has been the lack of bounded and analytic complex nonlinear activation functions in the complex domain, as stated by Liouville's theorem. To avoid the conflict between the boundedness and the analyticity of a nonlinear complex function in the complex domain, a number of ad hoc MLPs that include using two real-valued MLPs, one processing the real part and the other processing the imaginary part, have bee

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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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LIANG, XUN, and SHAOWEI XIA. "METHODS OF TRAINING AND CONSTRUCTING MULTILAYER PERCEPTRONS WITH ARBITRARY PATTERN SETS." International Journal of Neural Systems 06, no. 03 (1995): 233–47. http://dx.doi.org/10.1142/s0129065795000172.

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This paper presents two compensation methods for multilayer perceptrons (MLPs) which are very difficult to train by traditional Back Propagation (BP) methods. For MLPs trapped in local minima, compensating methods can correct the wrong outputs one by one using constructing techniques until all outputs are right, so that the MLPs can skip from the local minima to the global minima. A hidden neuron is added as compensation for a binary input three-layer perceptron trapped in a local minimum; and one or two hidden neurons are added as compensation for a real input three-layer perceptron. For a pe

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Xi, Yan Hui, and Hui Peng. "Training Multi-Layer Perceptrons with the Unscented Kalman Particle Filter." Advanced Materials Research 542-543 (June 2012): 745–48. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.745.

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Many Bayesian learning approaches to multi-layer perceptrons (MLPs) parameters optimization have been proposed such as the extended Kalman filter (EKF). In this paper, a sequential approach is applied to train the MLPs. Based on the particle filter, the approach named unscented Kalman particle filter (UPF) uses the unscented Kalman filter as proposal distribution to generate the importance sampling density. The UPF are devised to deal with the high dimensional parameter space that is inherent to neural network models. Simulation results show that the new algorithm performs better than traditio

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BUCHHOLZ, SVEN, and NICOLAS LE BIHAN. "POLARIZED SIGNAL CLASSIFICATION BY COMPLEX AND QUATERNIONIC MULTI-LAYER PERCEPTRONS." International Journal of Neural Systems 18, no. 02 (2008): 75–85. http://dx.doi.org/10.1142/s0129065708001403.

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For polarized signals, which arise in many application fields, a statistical framework in terms of quaternionic random processes is proposed. Based on it, the ability of real-, complex- and quaternionic-valued multi-layer perceptrons (MLPs) of performing classification tasks for such signals is evaluated. For the multi-dimensional neural networks the relevance of class label representations is discussed. For signal to noise separation it is shown that the quaternionic MLP yields an optimal solution. Results on the classification of two different polarized signals are also reported.

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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. Theref

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Mühlenbein, H. "Limitations of multi-layer perceptron networks - steps towards genetic neural networks." Parallel Computing 14, no. 3 (1990): 249–60. http://dx.doi.org/10.1016/0167-8191(90)90079-o.

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Achili, B., B. Daachi, Y. Amirat, A. Ali-Cherif, and M. E. Daâchi. "A stable adaptive force/position controller for a C5 parallel robot: a neural network approach." Robotica 30, no. 7 (2012): 1177–87. http://dx.doi.org/10.1017/s0263574711001354.

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SUMMARYThis paper presents an adaptive force/position controller for a parallel robot executing constrained motions. This controller, based on an MLPNN (or Multi-Layer Perceptron Neural Network), does not require the inverse dynamic model of the robot to derive the control law. A neural identification of the dynamic model of the robot is proposed to determine the principal components of the MLPNN input vector. The latter is used to compensate the dynamic effects arising from the robot–environment interaction and its parameters are adjusted according to an adaptation law based on the Lyapunov-a

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Payganeh, Gholam Hassan, Mehrdad Nouri Khajavi, Reza Ebrahimpour, and Ebrahim Babaei. "Machine Fault Diagnosis Using MLPs and RBF Neural Networks." Applied Mechanics and Materials 110-116 (October 2011): 5021–28. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.5021.

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-Fault detection and elimination in industrial machineries can help prevent loss of life and financial assets. In this study four common faults in rotating machineries namely: 1) Mass Unbalance 2) Angular Misalignment 3) Bearing Faults and 4) Mechanical Looseness have been considered. Each of these defects has been created separately on a test rig comprising of an electrical motor coupled to a rotor assembly. A Vibrotest 60 vibration spectrum analyzer has been used to collect velocity spectrum of the vibration on the bearings. Eleven characteristic features have been chosen to distinguish diff

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MÜGE, DURSUN, ŞENOL YAVUZ, BULGUN ENDER YAZGAN, and AKKAN TANER. "Neural network based thermal protective performance prediction of three-layered fabrics for firefighter clothing." Industria Textila 70, no. 01 (2019): 57–64. http://dx.doi.org/10.35530/it.070.01.1527.

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The firefighter protective clothing is comprised of three main layers; an outer shell, a moisture barrier and a thermal liner. This three-layered fabric structure provides protection against the fire and extremely hot environments. Various parameters such as fabric construction, weight, warp/weft count, warp/weft density, thickness, water vapour resistance of the fabric layers have effect on the protective performance as heat transfer through the firefighter clothing. In this study, it is aimed to examine the predictability of the heat transfer index of three-layered fabrics, as function of th

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Than, Nguyen Hien. "WATER QUALITY CLASSIFICATION BY ARTIFICIAL NEURAL NETWORK - A CASE STUDY OF DONG NAI RIVER, VIETNAM." Vietnam Journal of Science and Technology 55, no. 4C (2018): 297. http://dx.doi.org/10.15625/2525-2518/55/4c/12167.

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The Dong Nai River is the main source of supplied water for Ho Chi Minh City, Dong Nai, Binh Duong province and other areas. However, the water quality state of the Dong Nai River has been heavily pressured by discharged sources from urban areas, industrial zones, agricultural, domestic activities, etc. In this paper, the authors employed the artificial neural network model (ANNs) to classify water quality of Dong Nai River that apply a new tool to assess water quality in Vietnam. The monitoring data were used for eight years from 2007 to 2014 with 23 monitoring stations. Two neural network mo

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LEHTOKANGAS, MIKKO. "FEEDFORWARD NEURAL NETWORK WITH ADAPTIVE REFERENCE PATTERN LAYER." International Journal of Neural Systems 09, no. 01 (1999): 1–9. http://dx.doi.org/10.1142/s0129065799000022.

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A hybrid neural network architecture is investigated for modeling 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 reference pattern layer. Each unit in this new layer incorporates a reference pattern that is located somewhere in the space spanned by the input variables. The outputs of these units are the component wise-squared differences between the elements of a reference pattern and the inputs. The reference pattern layer has some resemblance to the hidden layer of

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Çaylak, Çağrı, and İlknur Kaftan. "Determination of near-surface structures from multi-channel surface wave data using multi-layer perceptron neural network (MLPNN) algorithm." Acta Geophysica 62, no. 6 (2014): 1310–27. http://dx.doi.org/10.2478/s11600-014-0207-8.

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Khotanzad, A., and C. Chung. "Application of multi-layer perceptron neural networks to vision problems." Neural Computing & Applications 7, no. 3 (1998): 249–59. http://dx.doi.org/10.1007/bf01414886.

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Jou, I. Chang, Shih-Shien You, and Long-Wen Chang. "Analysis of hidden nodes for multi-layer perceptron neural networks." Pattern Recognition 27, no. 6 (1994): 859–64. http://dx.doi.org/10.1016/0031-3203(94)90170-8.

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Bachtiar, Luqman R., Charles P. Unsworth, Richard D. Newcomb, and Edmund J. Crampin. "Multilayer Perceptron Classification of Unknown Volatile Chemicals from the Firing Rates of Insect Olfactory Sensory Neurons and Its Application to Biosensor Design." Neural Computation 25, no. 1 (2013): 259–87. http://dx.doi.org/10.1162/neco_a_00386.

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In this letter, we use the firing rates from an array of olfactory sensory neurons (OSNs) of the fruit fly, Drosophila melanogaster, to train an artificial neural network (ANN) to distinguish different chemical classes of volatile odorants. Bootstrapping is implemented for the optimized networks, providing an accurate estimate of a network's predicted values. Initially a simple linear predictor was used to assess the complexity of the data and was found to provide low prediction performance. A nonlinear ANN in the form of a single multilayer perceptron (MLP) was also used, providing a signific

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Nortje, Wimpie D., Johann E. W. Holm, Gerhard P. Hancke, Imre J. Rudas, and Laszlo Horvath. "Results of Bias-variance Tests on Multi-layer Perceptron Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 5 (2001): 300–305. http://dx.doi.org/10.20965/jaciii.2001.p0300.

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Training neural networks involves selection of a set of network parameters, or weights, on account of fitting a non-linear model to data. Due to the bias in the training data and small computational errors, the neural networks’ opinions are biased. Some improvement is possible when multiple networks are used to do the classification. This approach is similar to taking the average of a number of biased opinions in order to remove some of the bias that resulted from training. Bayesian networks are effective in removing some of the bias associated with training, but Bayesian techniques are tediou

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Śmieja, F. J., and H. Mühlenbein. "The geometry of multi-layer perceptron solutions." Parallel Computing 14, no. 3 (1990): 261–75. http://dx.doi.org/10.1016/0167-8191(90)90080-s.

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Savalia, Shalin, and Vahid Emamian. "Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks." Bioengineering 5, no. 2 (2018): 35. http://dx.doi.org/10.3390/bioengineering5020035.

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Sun, Wenzheng, Qichun Wei, Lei Ren, Jun Dang, and Fang-Fang Yin. "Adaptive respiratory signal prediction using dual multi-layer perceptron neural networks." Physics in Medicine & Biology 65, no. 18 (2020): 185005. http://dx.doi.org/10.1088/1361-6560/abb170.

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Bologna, Guido, and Yoichi Hayashi. "Characterization of Symbolic Rules Embedded in Deep DIMLP Networks: A Challenge to Transparency of Deep Learning." Journal of Artificial Intelligence and Soft Computing Research 7, no. 4 (2017): 265–86. http://dx.doi.org/10.1515/jaiscr-2017-0019.

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AbstractRule extraction from neural networks is a fervent research topic. In the last 20 years many authors presented a number of techniques showing how to extract symbolic rules from Multi Layer Perceptrons (MLPs). Nevertheless, very few were related to ensembles of neural networks and even less for networks trained by deep learning. On several datasets we performed rule extraction from ensembles of Discretized Interpretable Multi Layer Perceptrons (DIMLP), and DIMLPs trained by deep learning. The results obtained on the Thyroid dataset and the Wisconsin Breast Cancer dataset show that the pr

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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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K.S., Sree Ranjini. "A study on performance of MHDA in training MLPs." Engineering Computations 36, no. 6 (2019): 1820–34. http://dx.doi.org/10.1108/ec-05-2018-0216.

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Purpose In recent years, the application of metaheuristics in training neural network models has gained significance due to the drawbacks of deterministic algorithms. This paper aims to propose the use of a recently developed “memory based hybrid dragonfly algorithm” (MHDA) for training multi-layer perceptron (MLP) model by finding the optimal set of weight and biases. Design/methodology/approach The efficiency of MHDA in training MLPs is evaluated by applying it to classification and approximation benchmark data sets. Performance comparison between MHDA and other training algorithms is carrie

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Jadidi, Aydin, Raimundo Menezes, Nilmar de Souza, and Antonio Cezar de Castro Lima. "Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model." Energies 12, no. 10 (2019): 1891. http://dx.doi.org/10.3390/en12101891.

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Load forecasting is of crucial importance for smart grids and the electricity market in terms of the meeting the demand for and distribution of electrical energy. This research proposes a hybrid algorithm for improving the forecasting accuracy where a non-dominated sorting genetic algorithm II (NSGA II) is employed for selecting the input vector, where its fitness function is a multi-layer perceptron neural network (MLPNN). Thus, the output of the NSGA II is the output of the best-trained MLPNN which has the best combination of inputs. The result of NSGA II is fed to the Adaptive Neuro-Fuzzy I

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Jadidi, Aydin, Raimundo Menezes, Nilmar de Souza, and Antonio de Castro Lima. "A Hybrid GA–MLPNN Model for One-Hour-Ahead Forecasting of the Global Horizontal Irradiance in Elizabeth City, North Carolina." Energies 11, no. 10 (2018): 2641. http://dx.doi.org/10.3390/en11102641.

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The use of photovoltaics is still considered to be challenging because of certain reliability issues and high dependence on the global horizontal irradiance (GHI). GHI forecasting has a wide application from grid safety to supply–demand balance and economic load dispatching. Given a data set, a multi-layer perceptron neural network (MLPNN) is a strong tool for solving the forecasting problems. Furthermore, noise detection and feature selection in a data set with numerous variables including meteorological parameters and previous values of GHI are of crucial importance to obtain the desired res

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HAYASHI, YOICHI. "NEURAL NETWORK RULE EXTRACTION BY A NEW ENSEMBLE CONCEPT AND ITS THEORETICAL AND HISTORICAL BACKGROUND: A REVIEW." International Journal of Computational Intelligence and Applications 12, no. 04 (2013): 1340006. http://dx.doi.org/10.1142/s1469026813400063.

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This paper presents theoretical and historical backgrounds related to neural network rule extraction. It also investigates approaches for neural network rule extraction by ensemble concepts. Bologna pointed out that although many authors had generated comprehensive models from individual networks, much less work had been done to explain ensembles of neural networks. This paper carefully surveyed the previous work on rule extraction from neural network ensembles since 1988. We are aware of three major research groups i.e., Bologna' group, Zhou' group and Hayashi' group. The reason of these situ

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Zhang, Shiqing, Yueli Cui, Yuelong Chuang, Wenping Guo, Ying Chen, and Xiaoming Zhao. "Spoken Emotion Recognition by Combining Deep Belief Networks and Multi-layer Perceptron." International Journal of Multimedia and Ubiquitous Engineering 12, no. 2 (2017): 107–16. http://dx.doi.org/10.14257/ijmue.2017.12.2.08.

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Souza Filho, João B. O., and José Manoel Seixas. "Class‐modular multi‐layer perceptron networks for supporting passive sonar signal classification." IET Radar, Sonar & Navigation 10, no. 2 (2016): 311–17. http://dx.doi.org/10.1049/iet-rsn.2015.0179.

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Zhao, Zongyuan, Shuxiang Xu, Byeong Ho Kang, Mir Md Jahangir Kabir, Yunling Liu, and Rainer Wasinger. "Investigation and improvement of multi-layer perceptron neural networks for credit scoring." Expert Systems with Applications 42, no. 7 (2015): 3508–16. http://dx.doi.org/10.1016/j.eswa.2014.12.006.

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Tur, Rifat, and Serbay Yontem. "A Comparison of Soft Computing Methods for the Prediction of Wave Height Parameters." Knowledge-Based Engineering and Sciences 2, no. 1 (2021): 31–46. http://dx.doi.org/10.51526/kbes.2021.2.1.31-46.

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In the previous studies on the prediction of wave height parameters, only the significant wave height has been considered as the unknown parameter to be predicted. However, the other wave height parameters, which may be required for the design of coastal structures depending on their importance level, have been neglected. Therefore, in this study, novel soft computing methods were used to predict all wave height parameters required for the design of coastal structures. To this end, wave data were derived from a buoy located in Southwest Black Sea Coast. Then, Multi-layer Perceptron Neural Netw

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Rediniotis, O. K., and G. Chrysanthakopoulos. "Application of Neural Networks and Fuzzy Logic to the Calibration of the Seven-Hole Probe." Journal of Fluids Engineering 120, no. 1 (1998): 95–101. http://dx.doi.org/10.1115/1.2819670.

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The theory and techniques of Artificial Neural Networks (ANN) and Fuzzy Logic Systems (FLS) are applied toward the formulation of accurate and wide-range calibration methods for such flow-diagnostics instruments as multi-hole probes. Besides introducing new calibration techniques, part of the work’s objective is to: (a) apply fuzzy-logic methods to identify systems whose behavior is described in a “crisp” rather than a “linguistic” framework and (b) compare the two approaches, i.e., neural network versus fuzzy logic approach, and their potential as universal approximators. For the ANN approach

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Aguilar-Fuertes, Jose J., Francisco Noguero-Rodríguez, José C. Jaen Ruiz, Luis M. García-RAffi, and Sergio Hoyas. "Tracking Turbulent Coherent Structures by Means of Neural Networks." Energies 14, no. 4 (2021): 984. http://dx.doi.org/10.3390/en14040984.

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The behaviours of individual flow structures have become a relevant matter of study in turbulent flows as the computational power to allow their study feasible has become available. Especially, high instantaneous Reynolds Stress events have been found to dominate the behaviour of the logarithmic layer. In this work, we present a viability study where two machine learning solutions are proposed to reduce the computational cost of tracking such structures in large domains. The first one is a Multi-Layer Perceptron. The second one uses Long Short-Term Memory (LSTM). Both of the methods are develo

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Sutton, R., C. Johnson, and G. N. Roberts. "A Neural Auto-depth Controller for an Unmanned Underwater Vehicle." Journal of Navigation 50, no. 2 (1997): 292–302. http://dx.doi.org/10.1017/s0373463300023912.

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Artificial neural networks offer an alternative strategy for the nonlinear control of unmanned underwater vehicles (UUVS). This paper investigates the use of a multi-layered perceptron (MLP) network in controlling an UUV over a sea-bed profile and compares the use of applying chemotaxis learning to that of the more commonly employed back propagation algorithm. The results show that, for differing sized MLPs, the chemotaxis algorithm produces a successful controller over the sea-bed profile in an improved training time. Also it will be shown that, in the presence of noise and change in vehicle

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Malik, Anurag, Anil Kumar, Priya Rai, and Alban Kuriqi. "Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models." Climate 9, no. 2 (2021): 28. http://dx.doi.org/10.3390/cli9020028.

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Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e.,

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Le, Thai Hoang. "Applying Artificial Neural Networks for Face Recognition." Advances in Artificial Neural Systems 2011 (November 3, 2011): 1–16. http://dx.doi.org/10.1155/2011/673016.

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This paper introduces some novel models for all steps of a face recognition system. In the step of face detection, we propose a hybrid model combining AdaBoost and Artificial Neural Network (ABANN) to solve the process efficiently. In the next step, labeled faces detected by ABANN will be aligned by Active Shape Model and Multi Layer Perceptron. In this alignment step, we propose a new 2D local texture model based on Multi Layer Perceptron. The classifier of the model significantly improves the accuracy and the robustness of local searching on faces with expression variation and ambiguous cont

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Tamim, Nasser, M. Elshrkawey, Gamil Abdel Azim, and Hamed Nassar. "Retinal Blood Vessel Segmentation Using Hybrid Features and Multi-Layer Perceptron Neural Networks." Symmetry 12, no. 6 (2020): 894. http://dx.doi.org/10.3390/sym12060894.

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Segmentation of retinal blood vessels is the first step for several computer aided-diagnosis systems (CAD), not only for ocular disease diagnosis such as diabetic retinopathy (DR) but also of non-ocular disease, such as hypertension, stroke and cardiovascular diseases. In this paper, a supervised learning-based method, using a multi-layer perceptron neural network and carefully selected vector of features, is proposed. In particular, for each pixel of a retinal fundus image, we construct a 24-D feature vector, encoding information on the local intensity, morphology transformation, principal mo

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Harzallah, Salaheddine, R. Rebhi, M. Chabaat, and A. Rabehi. "Eddy current modelling using multi-layer perceptron neural networks for detecting surface cracks." Frattura ed Integrità Strutturale 12, no. 45 (2018): 147–55. http://dx.doi.org/10.3221/igf-esis.45.12.

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