Relevant bibliographies by topics / BACK PROPAGATION ALGORITHM (BPA)

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Academic literature on the topic 'BACK PROPAGATION ALGORITHM (BPA)'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "BACK PROPAGATION ALGORITHM (BPA)"

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R., Bhuvana, Purushothaman S., Rajeswari R., and Balaji R.G. "Development of combined back propagation algorithm and radial basis function for diagnosing depression patients." International Journal of Engineering & Technology 4, no. 1 (February 27, 2015): 244. http://dx.doi.org/10.14419/ijet.v4i1.4201.

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Depression is a severe and well-known public health challenge. Depression is one of the most common psychological problems affecting nearly everyone either personally or through a family member. This paper proposes neural network algorithm for faster learning of depression data and classifying the depression. Implementation of neural networks methods for depression data mining using Back Propagation Algorithm (BPA) and Radial Basis Function (RBF) are presented. Experimental data were collected with 21 depression variables used as inputs for artificial neural network (ANN) and one desired category of depression as the output variable for training and testing proposed BPA/RBF algorithms. Using the data collected, the training patterns, and test patterns are obtained. The input patterns are pre-processed and presented to the input layer of BPA/RBF. The optimum number of nodes required in the hidden layer of BPA/RBF is obtained, based on the change in the mean squared error dynamically, during the successive sets of iterations. The output of BPA is given as input to RBF. Through the combined topology, the work proves to be an efficient system for diagnosis of depression.
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Khanum, Afshan, S. Purushothaman, and P. Rajeswari. "Performance comparisons of the soft computing algorithms in lung segmentation and nodule identification." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 189. http://dx.doi.org/10.14419/ijet.v7i1.1.9287.

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This paper presents the implementation back propagation algorithm (BPA) and fuzzy logic(FL) in lung image segmentation and nodule identification. Lung image database consortium (LIDC) database images has been used. Features are extracted using statistical methods. These features are used for training the BPA and FL algorithms. Weights are stored in a file that is used for segmentation of the lung image. Subsequently, texture properties are used for nodule identification.
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Al-Araji, Ahmed Sabah, and Shaymaa Jafe'er Al-Zangana. "Design of New Hybrid Neural Controller for Nonlinear CSTR System based on Identification." Journal of Engineering 25, no. 4 (April 1, 2019): 70–89. http://dx.doi.org/10.31026/j.eng.2019.04.06.

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This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm namely Particle Swarm Optimization (PSO) algorithm. The numerical simulation results show that the hybrid NARMA-L2 controller with PSO algorithm is more accurate than BPA in terms of achieving fast learning and adjusting the parameters model with minimum number of iterations, minimum number of neurons in the hybrid network and the smooth output one step ahead prediction controller response for the nonlinear CSTR system without oscillation.
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Sujatha, K., N. Pappa, U. Siddharth Nambi, C. R. Raja Dinakaran, and K. Senthil Kumar. "Intelligent Parallel Networks for Combustion Quality Monitoring in Power Station Boilers." Advanced Materials Research 699 (May 2013): 893–99. http://dx.doi.org/10.4028/www.scientific.net/amr.699.893.

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This research work includes a combination of Fisher’s Linear Discriminant (FLD) analysis by combining Radial Basis Function Network (RBF) and Back Propagation Algorithm (BPA) for monitoring the combustion conditions of a coal fired boiler so as to control the air/fuel ratio. For this two dimensional flame images are required which was captured with CCD camera whose features of the images, average intensity, area, brightness and orientation etc., of the flame are extracted after pre-processing the images. The FLD is applied to reduce the n-dimensional feature size to 2 dimensional feature size for faster learning of the RBF. Also three classes of images corresponding to different burning conditions of the flames have been extracted from a continuous video processing. In this the corresponding temperatures, the Carbon monoxide (CO) emissions and other flue gases have been obtained through measurement. Further the training and testing of Parallel architecture of Radial Basis Function and Back Propagation Algorithm (PRBFBPA) with the data collected have been done and the performance of the algorithms is presented.
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Song, Shaoqiu, Jie Lu, Shiqi Xing, Sinong Quan, Junpeng Wang, Yongzhen Li, and Jing Lian. "Near Field 3-D Millimeter-Wave SAR Image Enhancement and Detection with Application of Antenna Pattern Compensation." Sensors 22, no. 12 (June 14, 2022): 4509. http://dx.doi.org/10.3390/s22124509.

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In this paper, a novel near-field high-resolution image focusing technique is proposed. With the emergence of Millimeter-wave (mmWave) devices, near-field synthetic aperture radar (SAR) imaging is widely used in automotive-mounted SAR imaging, UAV imaging, concealed threat detection, etc. Current research is mainly confined to the laboratory environment, thus ignoring the adverse effects of the non-ideal experimental environment on imaging and subsequent detection in real scenarios. To address this problem, we propose an optimized Back-Projection Algorithm (BPA) that considers the loss path of signal propagation among space by converting the amplitude factor in the echo model into a beam-weighting. The proposed algorithm is an image focusing algorithm for arbitrary and irregular arrays, and effectively mitigates sparse array imaging ghosts. We apply the 3DRIED dataset to construct image datasets for target detection, comparing the kappa coefficients of the proposed scheme with those obtained from classic BPA and Range Migration Algorithm (RMA) with amplitude loss compensation. The results show that the proposed algorithm attains a high-fidelity image reconstruction focus.
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Vinay, Kumar Jain. "A comparative analysis of neural network function: resilient back propagation algorithm (BPA) and radial basis functions (RBF) in multilingual environment." i-manager's Journal on Digital Signal Processing 10, no. 1 (2022): 9. http://dx.doi.org/10.26634/jdp.10.1.18639.

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The most convenient speech processing tool is Artificial Neural Networks (ANNs). The effectiveness has been tested with various real-time applications. The classifier using artificial neural networks identifies utterances based on features extracted from the speech signal. The proposed approach to multilingual speaker identification consists of two parts, such as a training part and a testing part. In the training part, the classifier is trained using speech feature vectors. The spoken language contains complete information, such as details about the content of the message and details about the speaker of that message. In the present work, the speech signal databases of different speakers in a multilingual environment were recorded in three Indian languages, i.e., Hindi, Marathi, and Rajasthani. The cepstral characteristics of the speech signal were extracted: Mel-Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC). The system is designed for speaker recognition through multilingual speech signals using MFCC, GFCC, and combined functions as acoustic characteristics. Training and testing were performed using the Neural Network (NN) function, robust Backpropagation Algorithm (BPA), and Radial Basis Functions (RBF), and the results were compared. The accuracy of the speaker identification system is 94.89% using BPA and 96.62% using the RBF neural network.
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EMAM, NAMEER N. EL, and RASHEED ABDUL SHAHEED. "COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC ALGORITHM WITH ADAPTIVE RELAXATION." International Journal on Artificial Intelligence Tools 17, no. 06 (December 2008): 1089–108. http://dx.doi.org/10.1142/s021821300800431x.

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A method based on neural network with Back-Propagation Algorithm (BPA) and Adaptive Smoothing Errors (ASE), and a Genetic Algorithm (GA) employing a new concept named Adaptive Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh points (AM) in fluid problems. AM based on reallocation scheme is implemented on different types of two steps channels by using a three layer neural network with GA. Results of numerical experiments using Finite Element Method (FEM) are discussed. Such discussion is intended to validate the process and to demonstrate the performance of the proposed learning system on three types of two steps channels. It appears that training is fast enough and accurate due to the optimal values of weights by using a few numbers of patterns. Results confirm that the presented neural network with the proposed GA consistently finds better solutions than the conventional neural network.
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Tayfour Ahmed, Amira, Altahir Mohammed, and Moawia Yahia. "Performance comparisons of artificial neural network algorithms in facial expression recognition." International Journal of Engineering & Technology 4, no. 4 (September 13, 2015): 465. http://dx.doi.org/10.14419/ijet.v4i4.5069.

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This paper presents methods for identifying facial expressions. The objective of this paper is to present a combination of texture oriented method with dimensional reduction and use for training the Single-Layer Neural Network (SLN), Back Propagation Algorithm (BPA) and Cerebellar Model Articulation Controller (CMAC) for identifying facial expressions. The proposed methods are called intelligent methods that can accommodate for the variations in the facial expressions and hence prove to be better for untrained facial expressions. Conventional methods have limitations that facial expressions should follow some constraints. To achieve the expression detection accuracy, Gabor wavelet is used in different angles to extract possible textures of the facial expression. The higher dimensions of the extracted texture features are further reduced by using Fisher’s linear discriminant function for increasing the accuracy of the proposed method. Fisher’s linear discriminant function is used for transforming higher-dimensional feature vector into a two-dimensional vector for training proposed algorithms. Different facial emotions considered are angry, disgust, happy, sad, surprise and fear are used. The performance comparisons of the proposed algorithms are presented.
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Venkaiah, Chintham, and Mallesham Dulla. "Static security based available transfer capability (ATC) computation for real-time power markets." Serbian Journal of Electrical Engineering 7, no. 2 (2010): 269–89. http://dx.doi.org/10.2298/sjee1002269v.

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In power system deregulation, the Independent System Operator (ISO) has the responsibility to control the power transactions and avoid overloading of the transmission lines beyond their thermal limits. To achieve this, the ISO has to update in real-time periodically Available Transfer Capability (ATC) index for enabling market participants to reserve the transmission service. In this paper Static Security based ATC has been computed for real-time applications using three artificial intelligent methods viz.: i) Back Propagation Algorithm (BPA); ii) Radial Basis Function (RBF) Neural network; and iii) Adaptive Neuro Fuzzy Inference System (ANFIS). These three different intelligent methods are tested on IEEE 24-bus Reliability Test System (RTS) and 75-bus practical System for the base case and critical line outage cases for different transactions. The results are compared with the conventional full AC Load Flow method for different transactions.
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Le, Duc Van. "APPLICABILITY OF ARTIFICIAL NEURAL NETWORK MODEL FOR SIMULATION OF MONTHLY RUNOFF IN COMPARISON WITH SOM OTHER TRADITIONAL MODELS." Science and Technology Development Journal 12, no. 4 (February 28, 2009): 94–106. http://dx.doi.org/10.32508/stdj.v12i4.2237.

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Artificial Neural Network (ANN) model along with Back Propagation Algorithm (BPA) has been applied in many fields, especially in hydrology and water resources management to simulate or forecast rainfall runoff process, discharge and water level - time series, and other hydrological variables. Several researches have recently been focusing to compare the applicability of ANN model with other theory-driven and data-driven approaches. The comparison of ANN with M5 model trees for rainfall-runoff forecasting, with ARMAX models for deriving flow series, with AR models and regression models for forecasting and estimating daily river flows have been carried out. The better results that were implemented by ANN model have been concluded. So, this research trend is continued for the comparison of ANN model with Tank, Harmonic, Thomas and Fiering models in simulation of the monthly runoffs at Dong Nai river basin, Viet Nam. The results proved ANN being the best choice among these models, if suitable and enough data sources were available.
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Dissertations / Theses on the topic "BACK PROPAGATION ALGORITHM (BPA)"

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Lowton, Andrew D. "A constructive learning algorithm based on back-propagation." Thesis, Aston University, 1995. http://publications.aston.ac.uk/10663/.

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There are been a resurgence of interest in the neural networks field in recent years, provoked in part by the discovery of the properties of multi-layer networks. This interest has in turn raised questions about the possibility of making neural network behaviour more adaptive by automating some of the processes involved. Prior to these particular questions, the process of determining the parameters and network architecture required to solve a given problem had been a time consuming activity. A number of researchers have attempted to address these issues by automating these processes, concentrating in particular on the dynamic selection of an appropriate network architecture. The work presented here specifically explores the area of automatic architecture selection; it focuses upon the design and implementation of a dynamic algorithm based on the Back-Propagation learning algorithm. The algorithm constructs a single hidden layer as the learning process proceeds using individual pattern error as the basis of unit insertion. This algorithm is applied to several problems of differing type and complexity and is found to produce near minimal architectures that are shown to have a high level of generalisation ability. (DX 187, 339)
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Xiao, Nancy Y. (Nancy Ying). "Using the modified back-propagation algorithm to perform automated downlink analysis." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/40206.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.
Includes bibliographical references (p. 121-122).
by Nancy Y. Xiao.
M.Eng.
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Sargelis, Kęstas. "Klaidos skleidimo atgal algoritmo tyrimai." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2009. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2009~D_20090630_094557-88383.

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Šiame darbe detaliai išanalizuotas klaidos skleidimo atgal algoritmas, atlikti tyrimai. Išsamiai analizuota neuroninių tinklų teorija. Algoritmui taikyti ir analizuoti sistemoje Visual Studio Web Developer 2008 sukurta programa su įvairiais tyrimo metodais, padedančiais ištirti algoritmo daromą klaidą. Taip pat naudotasi Matlab 7.1 sistemos įrankiais neuroniniams tinklams apmokyti. Tyrimo metu analizuotas daugiasluoksnis dirbtinis neuroninis tinklas su vienu paslėptu sluoksniu. Tyrimams naudoti gėlių irisų ir oro taršos duomenys. Atlikti gautų rezultatų palyginimai.
The present work provides an in-depth analysis of the error back-propagation algorithm, as well as information on the investigation carried out. A neural network theory has been analysed in detail. For the application and analysis of the algorithm in the system Visual Studio Web Developer 2008, a program has been developed with various investigation methods, which help to research into the error of the algorithm. For training neural networks, Matlab 7.1 tools have been used. In the course of the investigation, a multilayer artificial neural network with one hidden layer has been analysed. For the purpose of the investigation, data on irises (plants) and air pollution have been used. Comparisons of the results obtained have been made.
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Albarakati, Noor. "FAST NEURAL NETWORK ALGORITHM FOR SOLVING CLASSIFICATION TASKS." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2740.

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Classification is one-out-of several applications in the neural network (NN) world. Multilayer perceptron (MLP) is the common neural network architecture which is used for classification tasks. It is famous for its error back propagation (EBP) algorithm, which opened the new way for solving classification problems given a set of empirical data. In the thesis, we performed experiments by using three different NN structures in order to find the best MLP neural network structure for performing the nonlinear classification of multiclass data sets. A developed learning algorithm used here is the batch EBP algorithm which uses all the data as a single batch while updating the NN weights. The batch EBP speeds up training significantly and this is also why the title of the thesis is dubbed 'fast NN …'. In the batch EBP, and when in the output layer a linear neurons are used, one implements the pseudo-inverse algorithm to calculate the output layer weights. In this way one always finds the local minimum of a cost function for a given hidden layer weights. Three different MLP neural network structures have been investigated while solving classification problems having K classes: one model/K output layer neurons, K separate models/One output layer neuron, and K joint models/One output layer neuron. The extensive series of experiments performed within the thesis proved that the best structure for solving multiclass classification problems is a K joint models/One output layer neuron structure.
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Civelek, Ferda N. (Ferda Nur). "Temporal Connectionist Expert Systems Using a Temporal Backpropagation Algorithm." Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc278824/.

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Representing time has been considered a general problem for artificial intelligence research for many years. More recently, the question of representing time has become increasingly important in representing human decision making process through connectionist expert systems. Because most human behaviors unfold over time, any attempt to represent expert performance, without considering its temporal nature, can often lead to incorrect results. A temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems, has been introduced. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications. A temporal backpropagation algorithm which supports the model has been developed. The model along with the temporal backpropagation algorithm makes it extremely practical to define any artificial neural network application. Also, an approach that can be followed to decrease the memory space used by weight matrix has been introduced. The algorithm was tested using a medical connectionist expert system to show how best we describe not only the disease but also the entire course of the disease. The system, first, was trained using a pattern that was encoded from the expert system knowledge base rules. Following then, series of experiments were carried out using the temporal model and the temporal backpropagation algorithm. The first series of experiments was done to determine if the training process worked as predicted. In the second series of experiments, the weight matrix in the trained system was defined as a function of time intervals before presenting the system with the learned patterns. The result of the two experiments indicate that both approaches produce correct results. The only difference between the two results was that compressing the weight matrix required more training epochs to produce correct results. To get a measure of the correctness of the results, an error measure which is the value of the error squared was summed over all patterns to get a total sum of squares.
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Sisman, Yilmaz Nuran Arzu. "A Temporal Neuro-fuzzy Approach For Time Series Analysis." Phd thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/570366/index.pdf.

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The subject of this thesis is to develop a temporal neuro-fuzzy system for fore- casting the future behavior of a multivariate time series data. The system has two components combined by means of a system interface. First, a rule extraction method is designed which is named Fuzzy MAR (Multivari- ate Auto-regression). The method produces the temporal relationships between each of the variables and past values of all variables in the multivariate time series system in the form of fuzzy rules. These rules may constitute the rule-base in a fuzzy expert system. Second, a temporal neuro-fuzzy system which is named ANFIS unfolded in - time is designed in order to make the use of fuzzy rules, to provide an environment that keeps temporal relationships between the variables and to forecast the future behavior of data. The rule base of ANFIS unfolded in time contains temporal TSK(Takagi-Sugeno-Kang) fuzzy rules. In the training phase, Back-propagation learning algorithm is used. The system takes the multivariate data and the num- ber of lags needed which are the output of Fuzzy MAR in order to describe a variable and predicts the future behavior. Computer simulations are performed by using synthetic and real multivariate data and a benchmark problem (Gas Furnace Data) used in comparing neuro- fuzzy systems. The tests are performed in order to show how the system efficiently model and forecast the multivariate temporal data. Experimental results show that the proposed model achieves online learning and prediction on temporal data. The results are compared by other neuro-fuzzy systems, specifically ANFIS.
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Guan, Xing. "Predict Next Location of Users using Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263620.

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Predicting the next location of a user has been interesting for both academia and industry. Applications like location-based advertising, traffic planning, intelligent resource allocation as well as in recommendation services are some of the problems that many are interested in solving. Along with the technological advancement and the widespread usage of electronic devices, many location-based records are created. Today, deep learning framework has successfully surpassed many conventional methods in many learning tasks, most known in the areas of image and voice recognition. One of the neural network architecture that has shown the promising result at sequential data is Recurrent Neural Network (RNN). Since the creation of RNN, much alternative architecture have been proposed, and architectures like Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) are one of the popular ones that are created[5]. This thesis uses GRU architecture and features that incorporate time and location into the network to forecast people’s next location In this paper, a spatial-temporal neural network (ST-GRU) has been proposed. It can be seen as two parts, which are ST and GRU. The first part is a feature extraction algorithm that pulls out the information from a trajectory into location sequences. That process transforms the trajectory into a friendly sequence format in order to feed into the model. The second part, GRU is proposed to predict the next location given a user’s trajectory. The study shows that the proposed model ST-GRU has the best results comparing the baseline models.
Att förutspå vart en individ är på väg har varit intressant för både akademin och industrin. Tillämpningar såsom platsbaserad annonsering, trafikplanering, intelligent resursallokering samt rekommendationstjänster är några av de problem som många är intresserade av att lösa. Tillsammans med den tekniska utvecklingen och den omfattande användningen av elektroniska enheter har många platsbaserade data skapats. Idag har tekniken djupinlärning framgångsrikt överträffat många konventionella metoder i inlärningsuppgifter, bland annat inom områdena bild och röstigenkänning. En neural nätverksarkitektur som har visat lovande resultat med sekventiella data kallas återkommande neurala nätverk (RNN). Sedan skapandet av RNN har många alternativa arkitekturer skapats, bland de mest kända är Long Short Term Memory (LSTM) och Gated Recurrent Units (GRU). Den här studien använder en modifierad GRU där man bland annat lägger till attribut såsom tid och distans i nätverket för att prognostisera nästa plats. I det här examensarbetet har ett rumsligt temporalt neuralt nätverk (ST-GRU) föreslagits. Den består av två delar, nämligen ST och GRU. Den första delen är en extraktionsalgoritm som drar ut relevanta korrelationer mellan tid och plats som är inkorporerade i nätverket. Den andra delen, GRU, förutspår nästa plats med avseende på användarens aktuella plats. Studien visar att den föreslagna modellen ST-GRU ger bättre resultat jämfört med benchmarkmodellerna.
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Halabian, Faezeh. "An Enhanced Learning for Restricted Hopfield Networks." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42271.

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This research investigates developing a training method for Restricted Hopfield Network (RHN) which is a subcategory of Hopfield Networks. Hopfield Networks are recurrent neural networks proposed in 1982 by John Hopfield. They are useful for different applications such as pattern restoration, pattern completion/generalization, and pattern association. In this study, we propose an enhanced training method for RHN which not only improves the convergence of the training sub-routine, but also is shown to enhance the learning capability of the network. Particularly, after describing the architecture/components of the model, we propose a modified variant of SPSA which in conjunction with back-propagation over time result in a training algorithm with an enhanced convergence for RHN. The trained network is also shown to achieve a better memory recall in the presence of noisy/distorted input. We perform several experiments, using various datasets, to verify the convergence of the training sub-routine, evaluate the impact of different parameters of the model, and compare the performance of the trained RHN in recreating distorted input patterns compared to conventional RBM and Hopfield network and other training methods.
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Cheng, Martin Chun-Sheng, and pjcheng@ozemail com au. "Dynamical Near Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN) with Genetic Algorithm." Griffith University. School of Microelectronic Engineering, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030722.172812.

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Type-2 fuzzy logic system (FLS) cascaded with neural network, called type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of type-2 fuzzy linguistic process as the antecedent part and the two-layer interval neural network as the consequent part. A general T2FNN is computational intensive due to the complexity of type 2 to type 1 reduction. Therefore the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process (back propagation). It can also be shown both learning rates can not be both negative. Further, due to variation of the initial MF parameters, i.e. the spread level of uncertain means or deviations of interval Gaussian MFs, the performance of back propagation training process may be affected. To achieve better total performance, a genetic algorithm (GA) is designed to search better-fit spread rate for uncertain means and near optimal learnings for the antecedent part. Several examples are fully illustrated. Excellent results are obtained for the truck backing-up control and the identification of nonlinear system, which yield more improved performance than those using type-1 FNN.
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Cheng, Martin Chun-Sheng. "Dynamical Near Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN) with Genetic Algorithm." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/366350.

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Type-2 fuzzy logic system (FLS) cascaded with neural network, called type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of type-2 fuzzy linguistic process as the antecedent part and the two-layer interval neural network as the consequent part. A general T2FNN is computational intensive due to the complexity of type 2 to type 1 reduction. Therefore the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process (back propagation). It can also be shown both learning rates can not be both negative. Further, due to variation of the initial MF parameters, i.e. the spread level of uncertain means or deviations of interval Gaussian MFs, the performance of back propagation training process may be affected. To achieve better total performance, a genetic algorithm (GA) is designed to search better-fit spread rate for uncertain means and near optimal learnings for the antecedent part. Several examples are fully illustrated. Excellent results are obtained for the truck backing-up control and the identification of nonlinear system, which yield more improved performance than those using type-1 FNN.
Thesis (Masters)
Master of Philosophy (MPhil)
School of Microelectronic Engineering
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Books on the topic "BACK PROPAGATION ALGORITHM (BPA)"

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Lowton, Andrew David. A constructive learning algorithm based on back-propagation. Birmingham: Aston University. Department ofComputer Science and Applied Mathematics, 1995.

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Intelligent information retrieval using an inductive learning algorithm and a back-propagation neural network. Ann Arbor, Mich: University Microfilms International, 1995.

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Book chapters on the topic "BACK PROPAGATION ALGORITHM (BPA)"

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Lopes, Noel, and Bernardete Ribeiro. "GPU Implementation of the Multiple Back-Propagation Algorithm." In Intelligent Data Engineering and Automated Learning - IDEAL 2009, 449–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04394-9_55.

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Yoo, Jang-Hee, Jae-Woo Kim, and Jong-Uk Choi. "An Adaptive Training Method of Back-Propagation Algorithm." In Intelligent Systems Third Golden West International Conference, 531–36. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-7108-3_55.

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Satish Kumar, K., V. V. S. Sasank, K. S. Raghu Praveen, and Y. Krishna Rao. "Multilayer Perceptron Back propagation Algorithm for Predicting Breast Cancer." In Advances in Intelligent Systems and Computing, 41–53. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5400-1_5.

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Chen, D. S., and R. C. Jain. "A robust back-propagation learning algorithm for function approximation." In Artificial Intelligence Frontiers in Statistics, 217–40. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4537-2_17.

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Paugam-Moisy, Hélène. "Optimal speedup conditions for a parallel back-propagation algorithm." In Parallel Processing: CONPAR 92—VAPP V, 719–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55895-0_474.

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Mao, Min, and Daowu Pei. "Fuzzy Adaptive Back Propagation Model Based on Genetic Algorithm." In Recent Advances in Computer Science and Information Engineering, 665–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25781-0_97.

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Nawi, Nazri Mohd, Muhammad Zubair Rehman, and Abdullah Khan. "A New Bat Based Back-Propagation (BAT-BP) Algorithm." In Advances in Intelligent Systems and Computing, 395–404. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01857-7_38.

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Ren, Gang, Qingsong Hua, Pan Deng, and Chao Yang. "FP-MRBP: Fine-grained Parallel MapReduce Back Propagation Algorithm." In Artificial Neural Networks and Machine Learning – ICANN 2017, 680–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68612-7_77.

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Latifi, Nasim, and Ali Amiri. "Partial and Random Updating Weights in Error Back Propagation Algorithm." In Communications in Computer and Information Science, 414–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27337-7_39.

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Nawi, Nazri Mohd, R. S. Ransing, Mohd Najib Mohd Salleh, Rozaida Ghazali, and Norhamreeza Abdul Hamid. "An Improved Back Propagation Neural Network Algorithm on Classification Problems." In Database Theory and Application, Bio-Science and Bio-Technology, 177–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17622-7_18.

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Conference papers on the topic "BACK PROPAGATION ALGORITHM (BPA)"

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Xi, Wu-Dong, Ling Huang, Chang-Dong Wang, Yin-Yu Zheng, and Jianhuang Lai. "BPAM: Recommendation Based on BP Neural Network with Attention Mechanism." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/542.

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Inspired by the significant success of deep learning, some attempts have been made to introduce deep neural networks (DNNs) in recommendation systems to learn users' preferences for items. Since DNNs are well suitable for representation learning, they enable recommendation systems to generate more accurate prediction. However, they inevitably result in high computational and storage costs. Worse still, due to the relatively small number of ratings that can be fed into DNNs, they may easily lead to over-fitting. To tackle these problems, we propose a novel recommendation algorithm based on Back Propagation (BP) neural network with Attention Mechanism (BPAM). In particular, the BP neural network is utilized to learn the complex relationship of the target users and their neighbors. Compared with deep neural network, the shallow neural network, i.e., BP neural network, can not only reduce the computational and storage costs, but also prevent the model from over-fitting. In addition, an attention mechanism is designed to capture the global impact on all nearest target users for each user. Extensive experiments on eight benchmark datasets have been conducted to evaluate the effectiveness of the proposed model.
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Bin, Sun, Zhang Jin, and Zhang Shaoji. "An Investigation of Artificial Neural Network (ANN) in Quantitative Fault Diagnosis for Turbofan Engine." In ASME Turbo Expo 2000: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/2000-gt-0032.

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This paper is aimed at investigating two kinds of Artificial Neural Network (ANN) applied to quantitative fault diagnosis of turbofan engine gas path components. Among them, one is Back Propagation neural Network (BPN) and the other is Adaptive Probabilistic Neural Network (APNN). Using BPN in order to achieve quantitative fault diagnosis, number of training samples will increase greatly which may lead to the difficulty of iteration convergence. A new learning rule named hybrid rule is introduced to avoid the algorithm falling into static areas and expedite convergence. Recently, a new method to improve the adaptability of multi-layer feed-forward neural network has been developed by the application of Radial Basis Function (RBF). In this paper, the APNN is put forward based on the theory of radial basis function, Bayesian estimation and normal distribution hypothesis of information. It is proposed that the adaptability of APNN can be obtained by applying maximum-likelihood estimation of the output of test case based on a posteriori probability of its input. The investigation shows that BPN and APNN have their own advantages and disadvantages. BPN has faster diagnostic speed and fits the requirement of quantitative diagnosis for single fault. APNN is more adaptive and fit better to quantitative diagnosis for multiple faults.
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Kothari, R., P. Klinkhachorn, and R. S. Nutter. "An accelerated back propagation training algorithm." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170398.

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Roy, Soumava Kumar, and Crefeda Faviola Rodrigues. "Echo Canceller Using Error Back Propagation Algorithm." In 2014 International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2014. http://dx.doi.org/10.1109/iscmi.2014.33.

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Makram-Ebeid, Sirat, and Viala. "A rationalized error back-propagation learning algorithm." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118725.

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Maalej, Z., V. Sleiffer, E. Timmers, A. Napoli, M. Kuschnerov, B. Spinnler, and N. Hanik. "Reduced complexity for back-propagation method algorithm." In 2011 IEEE Photonics Conference (IPC). IEEE, 2011. http://dx.doi.org/10.1109/pho.2011.6110738.

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Nakagawa, Masashi, Takashi Inoue, and Yoshifumi Nishio. "CNN template design using back propagation algorithm." In 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010). IEEE, 2010. http://dx.doi.org/10.1109/cnna.2010.5430327.

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Mishra, B. K., S. K. Singh, and S. Bhala. "Breast cancer diagnosis using back-propagation algorithm." In the International Conference & Workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1980022.1980123.

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Varkonyi-Koczy, Annamaria R., and Balazs Tusor. "Improved back-propagation algorithm for neural network training." In 2011 IEEE 7th International Symposium on Intelligent Signal Processing - (WISP 2011). IEEE, 2011. http://dx.doi.org/10.1109/wisp.2011.6051720.

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Fukumi and Omatu. "A new back-propagation algorithm with coupled neuron." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118442.

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Reports on the topic "BACK PROPAGATION ALGORITHM (BPA)"

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Deller, Jr, Hunt J. R., and S. D. A Simple 'Linearized' Learning Algorithm Which Outperforms Back-Propagation. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada249697.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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