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CONVERGENCE OF back-propagation IN NEURAL NETWORKS USING A LOG-LIKELIHOOD COST FUNCTION

ELECTRONICS LETTERS 1990年第23期26卷 1964-1965页

作者： HOLT, MJJ SEMNANI, S Dept. of Electron. & Electr. Eng. Loughborough Univ. of Technol. UK

A variation of the back-propagation algorithm is described, using a log-likelihood cost function. Appropriate choices of learning parameters are discussed. An example is given where the range of initial weights leadin... 详细信息

关键词： back-propagation algorithm convergence convergence of back-propagation initial weights iterations reduction log-likelihood cost function neural nets neural networks

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Memristive crossbar-based circuit design of back-propagation neural network with synchronous memristance adjustment

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COMPLEX & INTELLIGENT SYSTEMS 2024年第4期10卷 4883-4896页

作者： Yang, Le Ding, Zhixia Xu, Yanyang Zeng, Zhigang Wuhan Inst Technol Sch Elect & Informat Engn Guanggu 1st Rd Wuhan 430205 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Luoyu Rd Wuhan 430074 Hubei Peoples R China

The performance improvement of CMOS computer fails to meet the enormous data processing requirement of artificial intelligence progressively. The memristive neural network is one of the most promising circuit hardwares to make a breakthrough. This paper proposes a novel memristive synaptic circuit that is composed of four MOS transistors and one memristor (4T1M). The 4T1M synaptic circuit provides flexible control strategies to change memristance or respond to the input signal. Applying the 4T1M synaptic circuit as the cell of memristive crossbar array, based on the structure and algorithm of the back-propagation (BP) neural network, this paper proposes circuit design of the memristive crossbar-based BP neural network. By reusing the 4T1M memristive crossbar array, the computations in the forward-propagation process and back-propagation process of BP neural network are accomplished on the memristive crossbar-based circuit to accelerate the computing speed. The 4T1M memristive crossbar array can change all the cells' memristance at a time, accordingly, the memristive crossbar-based BP neural network can realize synchronous memristance adjustment. The proposed memristive crossbar-based BP neural network is then evaluated through experiments involving XOR logic operation, iris classification, and MNIST handwritten digit recognition. The experimental results present fewer iterations or higher classification accuracies. Further, the comprehensive comparisons with the existing memristive BP neural networks highlight the advantages of the proposed memristive crossbar-based BP neural network, which achieves the fastest memristance adjustment speed using relatively few components.

关键词： Memristive circuit Memristive neural network back-propagation algorithm Crossbar array Synchronous weight training

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back-propagation and K-means algorithms comparison

Back-propagation and K-means algorithms comparison

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8th International Conference on Signal Processing

作者： Skorpil, Vladislav Stastny, Jiri Brno Univ Technol Dept Telecommun Purkynova 118 Brno 61200 Czech Republic Brno Univ Technol Dept Comp Sci & Automat Purkynova 118 Brno 61200 Czech Republic

ISBN: (纸本)9780780397361

The paper describes the application of algorithms for object classification by using artificial neural networks. The MLP (Multi Layer Perceptron) and RBF (Radial Basis Function) neural networks were used. We compared results obtained by a using of learning algorithms back-propagation (BP) and K-Means. The real technological scene for object classification was simulated with digitization of two-dimensional pictures. The principles and algorithms given below have been used in an application that was developed at Brno University of Technology.

关键词： image processing back-propagation algorithm K-means algorithm

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Beyond back-propagation Learning for Diabetic Detection: Convergence Comparison of Gradient Descent, Momentum and Adaptive Learning Rate 1

Beyond Back-Propagation Learning for Diabetic Detection: Con...

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1st International Conference on Informatics and Computational Sciences (ICICoS)

作者： Endah, Sukmawati N. Widodo, Aris P. Fariq, Muhammad L. Nadianada, Shavira I. Maulana, Fadil Univ Diponegoro Dept Informat Comp Sci Semarang Indonesia

ISBN: (纸本)9781538609033

back-propagation (BP) algorithm is one of the classification algorithms in Artificial Neural Network (ANN). back-propagation still has problems with the training process, where learning convergence of gradient descent is very slow. Increase convergence rate can be done with the use of Adaptive Learning Rate and momentum optimization. Previous research that combining both optimization techniques was able to accelerate the training process. Therefore, this research will compare the convergence of gradient descent, gradient descent + momentum, gradient descent + adaptive learning rate and gradient descent + momentum + adaptive learning rate for diabetic detection. Based on the experimental results, it is known that gradient descent has better performance than others in terms of convergence velocity and result of TPR and FPR value. Training algorithm with combining of gradient descent + momentum + adaptive learning rate faster convergent compared to gradient + momentum or gradient + adaptive learning rate.

关键词： back-propagation algorithm gradient descent adaptive learning rate momentum diabetic detection

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Nonlinear inverse modeling of sensor based on back-propagation fuzzy logical system

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Journal of Pharmaceutical Analysis 2007年第1期19卷 14-17页

作者：李军刘君华 School of Automation and Electrical Engineering Lanzhou Jiaotong University School of Electrical Engineering Xi'an Jiaotong University

Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on back-propagation Fuzzy Logical System(BP FS) is *** The BP FS is a computationally efficient nonlinear universal approximator,which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence *** The neuro-fuzzy hybrid system,*** FS,is then applied to construct nonlinear inverse model of pressure *** experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation,and thus the performance of pressure sensor is significantly *** The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.

关键词： sensor inverse modeling fuzzy logical system back-propagation algorithm

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Developing energy forecasting model using hybrid artificial intelligence method

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Journal of Central South University 2015年第8期22卷 3026-3032页

作者： Shahram Mollaiy-Berneti Young Researchers and Elites Club Bandar Abbas Branch Islamic Azad University

An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation(BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand(gross domestic product(GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand(population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.

关键词： energy demand artificial neural network back-propagation algorithm imperialist competitive algorithm

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Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第9期37卷 6233-6239页

作者： Khashman, Adnan Near East Univ Intelligent Syst Res Grp TR-10 Lefkosa Mersin Turkey

This paper describes a credit risk evaluation system that uses supervised neural network models based on the back propagation learning algorithm. We train and implement three neural networks to decide whether to approve or reject a credit application. Credit scoring and evaluation is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. The neural networks are trained using real world credit application cases from the German credit approval datasets which has 1000 cases;each case with 24 numerical attributes;based on which an application is accepted or rejected. Nine learning schemes with different training-to-validation data ratios have been investigated, and a comparison between their implementation results has been provided. Experimental results will suggest which neural network model, and under which learning scheme, can the proposed credit risk evaluation system deliver optimum performance;where it may be used efficiently, and quickly in automatic processing of credit applications. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Credit risk evaluation Artificial neural networks (ANN) back-propagation algorithm Neural learning schemes Finance and banking

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A cloning approach to classifier training

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2002年第6期32卷 746-752页

作者： Al-Alaoui, MA Mouci, R Mansour, MM Ferzli, R Amer Univ Beirut Dept Elect & Comp Engn Beirut Lebanon Cent Bank Lebanon Beirut Lebanon Univ Illinois Coordinated Sci Lab Elect & Comp Engn Dept Urbana IL 61801 USA

The Al-Alaoui algorithm is a weighted mean-square error (MSE) approach to pattern recognition. It employs cloning of the erroneously classified samples to increase the population of their corresponding classes. The algorithm was originally developed for linear classifiers. In this paper, the algorithm is extended to multilayer neural networks which may be used as nonlinear classifiers. It is also shown that the application of the Al-Alaoui algorithm to multilayer neural networks speeds up the convergence of the back-propagation algorithm.

关键词： Al-Alaoui algorithm back-propagation algorithm Bayes classifier character recognition Levenberg-Marquardt algorithm neural networks pattern classification

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Modeling of Mechanical Properties of Clay-Reinforced Polymer Nanocomposites Using Deep Neural Network

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MATERIALS 2020年第19期13卷 4266页

作者： Zazoum, Bouchaib Triki, Ennouri Bachri, Abdel Prince Mohammad Bin Fahd Univ Dept Mech Engn Al Khobar 31952 Saudi Arabia Coll Communautaire Nouveau Brunswick CCNB INNOV Caraquet NB E1W 1B6 Canada Southern Arkansas Univ Dept Phys & Engn Magnolia AR 71753 USA

Due to the non-linear characteristics of the processing parameters, predicting the desired properties of nanocomposites using the conventional regression approach is often unsatisfactory. Thus, it is essential to use a machine learning approach to determine the optimum processing parameters. In this study, a backpropagation deep neural network (DNN) with nanoclay and compatibilizer content, and processing parameters as input, was developed to predict the mechanical properties, including tensile modulus and tensile strength, of clay-reinforced polyethylene nanocomposites. The high accuracy of the developed model proves that DNN can be used as an efficient tool for predicting mechanical properties of the nanocomposites in terms of four independent parameters.

关键词： polymer clay nanocomposites mechanical properties deep neural network back-propagation algorithm

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Neuro-genetic approach on logistic model based software reliability prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第10期42卷 4709-4718页

作者： Roy, Pratik Mahapatra, G. S. Dey, K. N. Univ Calcutta Dept Comp Sci & Engn Kolkata 700009 India Natl Inst Technol Puducherry Dept Math Karaikal 609605 India

In this paper, we propose a multi-layer feedforward artificial neural network (ANN) based logistic growth curve model (LGCM) for software reliability estimation and prediction. We develop the ANN by designing different activation functions for the hidden layer neurons of the network. We explain the ANN from the mathematical viewpoint of logistic growth curve modeling for software reliability. We also propose a neuro-genetic approach for the ANN based LGCM by optimizing the weights of the network using proposed genetic algorithm (GA). We first train the ANN using back-propagation algorithm (BPA) to predict software reliability. After that, we use the proposed GA to train the ANN by globally optimizing the weights of the network. The proposed ANN based LGCM is compared with the traditional Non-homogeneous Poisson process (NHPP) based software reliability growth models (SRGMs) and ANN based software reliability models. We present the comparison between the two training algorithms when they are applied to train the proposed ANN to predict software reliability. The applicability of the different approaches is explained through three real software failure data sets. Experimental results demonstrate that the proposed ANN based LGCM has better fitting and predictive capability than the other NHPP and ANN based software reliability models. It is also noted that when the proposed GA is employed as the learning algorithm to the ANN, the proposed ANN based LGCM gives more fitting and prediction accuracy i.e. the proposed neuro-genetic approach to the LGCM provides utmost predictive validity. Proposed model can be applied during software testing time to get better software reliability estimation and prediction than the other traditional NHPP and ANN based software reliability models. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Artificial neural network Genetic algorithm back-propagation algorithm Logistic growth curve model Software reliability Prediction

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