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5th International Conference on Wavelet Analysis and Pattern Recognition

作者： Yan, Tai-Shan Tao, Yong-Qing Cui, Du-Wu Xian Univ Technol Sch Engn & Comp Sci Xian 710048 Shaanxi Peoples R China

ISBN: (纸本)9781424410651

Considering the limitation such as premature convergence and low, local convergence speed of genetic algorithm, some improvements were made for classical genetic algorithm. Firstly, a help operator was used to help individuals of population according to the given probability. Secondly, the genetic individuals were separated into male individuals and female individuals, and consanguinity was fused into individuals. Two individuals with different sex could reproduce the next generation only if they were distant consanguinity individuals. Based on this improved genetic algorithm, an evolved neural network algorithm named IGI-bp algorithm was proposed. In this algorithm, genetic algorithm was used to optimize and design the structure, the initial weights and thresholds, the training ratio and momentum factor of neural network roundly. The disadvantage of neural networks that their structure and parameters were decided stochastically or by one's experience was overcome in this way, and the surge of algorithm was restrained. IGA-bp algorithm was used to recognize handwritten numerals, a recognition model of handwritten numerals based on bp neural network was found, and the handwritten numeral recognition scheme based on IGA-bp algorithm was proposed. The experimental results show that this algorithm is better than SGA-bp algorithm and traditional bp algorithm in both speed and precision of convergence, 14,e can obtain a better recognition effect using this algorithm.

关键词： genetic algorithm neural network bp algorithm handwritten numeral recognition

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Applications of ANNs in geotechnical engineering

Applications of ANNs in geotechnical engineering

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8th International Conference on Electronic Measurement and Instruments

作者： Chen Xiangjun Gao Zhanfeng Shijiazhuang Railway Inst Shijiazhuang 050043 Peoples R China

Artificial neural networks (ANNs) was developed very quickly and applied very widely in recent years due to its strong ability to solve the nonlinear problems. The artificial neural network-based method was also widely applied to the geotechnical engineering. The complexity of the geotechnical engineering problems because of the strong nonlinear relationship between knows and unknowns of the problems can be mapped very well by artificial neural networks. Researches on the application of artificial neural network in geotechnical engineering are reviewed and appraised in this paper. All the networks mentioned are trained with the back-propagation algorithm which is widely used by a great number of researchers. Research reveals that the method is feasible and it will be interested for more geotechnical engineers.

关键词： artificial neural network bp algorithm geotechnical engineering

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A NEW algorithm FOR THE THEORETICAL ENERGY LOSS CALCULATION OF THE DISTRIBUTION NETWORK BASED ON ARTIFICIAL NEURAL NETWORKS

A NEW ALGORITHM FOR THE THEORETICAL ENERGY LOSS CALCULATION ...

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： HONG-RUI WANG GUO-JIE YANG XIU-LING LIU Department of Electronic and Informational Engineering Hebei University Baoding 071002 China

In this paper, several normal calculation algorithms for the theoretical energy loss of the power distribution network were simply *** analyzing their limitedness in using and applying the technology of artificial neural network (ANN), a novel bp based optimum calculation algorithm for the theoretical energy loss of the power distribution network was *** practical applications showed that the efficiency and veracity of the calculation for theoretical energy loss was improved.

关键词： Distribution networks Theoretical energy losses Artificial Neural Networks bp algorithm

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Applications of ANNs in Geotechnical Engineering

Applications of ANNs in Geotechnical Engineering

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2007 8th International Conference on Electronic Measurement & Instruments（ICEMI’2007）

作者： Chen Xiangjun Gao Zhanfeng Shijiazhuang Railway Institute, Shijiazhuang, 050043,China

Artificial neural networks ANNs was developed very quickly and applied very widely in recent years due to its strong ability to solve the nonlinear problems. The artificial neural network-based method was also widely applied to the geotechnical engineering. The complexity of the geotechnical engineering problems because of the strong nonlinear relationship between knows and unknowns of the problems can be mapped very well by artificial neural networks. Researches on the application of artificial neural network in geotechnical engineering are reviewed and appraised in this paper. All the networks mentioned are trained with the back-propagation algorithm which is widely used by a great number of researchers. Research reveals that the method is feasible and it will be interested for more geotechnical engineers.

关键词： Artificial neural network bp algorithm geotechnical engineering

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APPLICATION OF COMBINATORIAL PROBABILISTIC NEURAL NETWORK IN FAULT DIAGNOSIS OF POWER TRANSFORMER

APPLICATION OF COMBINATORIAL PROBABILISTIC NEURAL NETWORK IN...

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： YONG-CHUN LIANG XIAO-YUN SUN DONG-HUI LIU HUI-QIN SUN Department of electrical engineering Hebei University of Science and Technology Shijiazhuang 050054 China

Probabilistic Neural Network (PNN) overcame the shortcomings of entrapment in local optimum, slow convergence rate which was in bp *** enough training samples, PNN obtained the optimal result of Bayesian *** of the fast training rate, the training samples can be added into PNN at any ***, PNN is fit to diagnose the fault of power transformer and has *** order to improve the classification accuracy, the conception of combination is introduced into *** fault diagnosis of power transformer is consisted of 4 Probability neural networks in this *** is used to classify the normal and fault PNN2 is used to classify the heat fault and partial discharge (PD) fault PNN3 is used to classify the general overheating fault and severe overheating fault PNN4 is used to classify the partial discharge fault, and energy sparking or arcing *** example shows that the effect of combinatorial PNN is a good classifier in the fault diagnosis of power *** combinatorial PNN has better diagnosis accuracy than bpNN and FUZZY algorithm.

关键词： Probabilistic neural network bp algorithm Bayesian rules Diagnosis Power transformer

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CONSTRUCTION AND APPLICATION OF PSO NEURAL NETWORK FOR PERSONAL CREDIT SCORING

CONSTRUCTION AND APPLICATION OF PSO NEURAL NETWORK FOR PERSO...

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2007中国宁波国际计算机科学与技术学术大会

作者： Minghui Jiang Xuchuan Yuan School of Management Harbin Institute of Technology

Personal credit scoring plays an important role in keeping away from credit risks in consumer credit market of commercial *** to the insufficiency of bp neural network,this paper constructs a PSO neural network model for personal credit scoring by using PSO to train the network's *** with bp network,PSO network gets higher classification accuracy in testing samples and shows stronger ***'s more,PSO network is more applicable for personal credit scoring in a dynamic credit environment.

关键词： Personal credit scoring Neural network Particle swarm optimization bp algorithm

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RESEARCH ON HANDWRITTEN NUMERAL RECOGNITION METHOD BASED ON IMPROVED GENETIC algorithm AND NEURAL NETWORK

RESEARCH ON HANDWRITTEN NUMERAL RECOGNITION METHOD BASED ON ...

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2007 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR 2007), vol.3

作者： TAI-SHAN YAN YONG-QING TAO DU-WU CUI School of Computer Science and Engineering Xi''an University of Technology Xi'an Shaanxi China Department of Computer Science Hunan Institute of Technology Yueyang Hunan China

Considering the limitation such as premature convergence and low local convergence speed of genetic algorithm, some improvements were made for classical genetic algorithm. Firstly, a help operator was used to help individuals of population according to the given probability. Secondly, the genetic individuals were separated into male individuals and female individuals, and consanguinity was fused into individuals. Two individuals with different sex could reproduce the next generation only if they were distant consanguinity individuals. Based on this improved genetic algorithm, an evolved neural network algorithm named IGA-bp algorithm was proposed. In this algorithm, genetic algorithm was used to optimize and design the structure, the initial weights and thresholds, the training ratio and momentum factor of neural network roundly. The disadvantage of neural networks that their structure and parameters were decided stochastically or by one's experience was overcome in this way, and the surge of algorithm was restrained. IGA-bp algorithm was used to recognize handwritten numerals, a recognition model of handwritten numerals based on bp neural network was found, and the handwritten numeral recognition scheme based on IGA-bp algorithm was proposed. The experimental results show that this algorithm is better than SGA-bp algorithm and traditional bp algorithm in both speed and precision of convergence, We can obtain a better recognition effect using this algorithm.

关键词： Genetic algorithm Neural network bp algorithm Handwritten numeral recognition

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Integrated risk early warning of institutional investors based on bp network

Integrated risk early warning of institutional investors bas...

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13th International Conference on Management Science and Engineering

作者： Tian Qing-feng Yang Nai-ding Jiang Ji-jiao Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China

ISBN: (纸本)7560323553

This study was aimed at developing an integrated risk early warning pattern of institutional investors with bp network. By indicating advantages of bp network in dealing with various risk factors, it's formed an integrated risk early warning model, which is optimized structurally through training and testing. It presents a type of an improved bp network algorithm with adjusting function by fuzzy inference and applies it to the optimization of risk early warning indexes. The simulation shows that this model will be well on describing the holistic risk status, distilling key risk factors, and adopting systematic risk countermeasures for institutional investors.

关键词： artificial neural network bp algorithm fuzzy inference system risk management

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Multi-step predictive control with TDbp method for pneumatic position servo system

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2006年第1期28卷 53-68页

作者： Wang, XS Cheng, YH Sun, W China Univ Min & Technol Sch Informat & Elect Engn Jiangsu 221008 Peoples R China

This paper presents a new multi-step predictive controller based on neural networks and researches the adaptability of the predictive controller for a pneumatic position servo system which has some typical characteristics of non-linearity and time-varying. A diagonal recurrent neural network (DRNN) is used to predict the system Output Of the multi-step ahead directly. According to the intrinsic defects of a back-propagation (bp) algorithm that cannot update network weights incrementally, a new hybrid learning algorithm combining the temporal differences (TD) method with the bp algorithm to train the DRNN is put forward. A three-layer feedforward bp neural network is used as a non-linear rolling optimal controller to realize the optimization of control input of the next step according to a single-value predictive control algorithm to Simplify Computation. Simulation and experimental results indicate that the proposed predictive controller is Suitable for real-time control of a pneumatic position servo system because of its characteristics of a simple algorithm, fast calculation of the control input and good tracking effects.

关键词： bp algorithm multi-step predictive pneumatic position servo system TD method

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Kikuchi approximation method for joint decoding of LDPC codes and partial-response channels

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IEEE TRANSACTIONS ON COMMUNICATIONS 2006年第7期54卷 1149-1153页

作者： Pakzad, Payam Anantharam, Venkat Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Ecole Polytech Fed Lausanne Sch Comp & Commun Engn CH-1015 Lausanne Switzerland

In this letter, we apply the Kikuchi approximation method to the problem of joint decoding of a low-density parity-check code and a partial-response channel. The Kikuchi method is, in general, more powerful than the conventional loopy belief propagation (bp) algorithm, and can produce better approximations to an underlying inference problem. We will first review the Kikuchi approximation method and the generalized bp algorithm, which is an iterative message-passing algorithm based on this method. We will then report simulation results which show that the Kikuchi method outperforms the best conventional iterative method.

关键词： .Gbp message-passing marginals belief propagation Partial-Response LDPC Codes bp algorithm Joint Decoding

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