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International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Lin, Yingjian Chen, Xiaoji Bohai Univ Teaching & Res Inst Coll Comp Jinzhou Peoples R China

ISBN: (纸本)9783038350125

bp learning algorithm has advantage of simple structure, easy to implement and so on, it has gained wide application in the malfunction diagnosis and pattern recognition etc.. For bp algorithm is easy to fall into local minima shortcoming cites simulated annealing algorithm. Firstly, study the basic idea of bp learning algorithm and its simple mathematical representation;Then, research simulated annealing algorithm theory and annealing processes;Finally, the study makes bp algorithm combine with simulated annealing algorithm to form a hybrid optimization algorithm of simulated annealing algorithm based on genetic and improved bp algorithm, and gives specific calculation steps. The results show that the content of this study give full play to their respective advantages of two algorithms, make best use of the advantages and bypass the disadvantages, whether in academic or in the application it has a very important significance.

关键词： bp neural network bp learning algorithm improved bp algorithm simulated annealing algorithm

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Study of Full Interval Feed-forward Neural Network 28

Study of Full Interval Feed-forward Neural Network

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第28届中国控制与决策会议

作者： GUAN Shou-ping LIANG Rong-ye College of Information Science and Engineering Northeastern University

ISBN: (纸本)9781467397155

This paper proposes an architecture and bp learning algorithm for interval feed-forward neural networks(IFNNs)with all parameters setting to intervals,which can be called full interval feed-forward neural network(FIFNN),to handle uncertain information represented in interval-valued *** order to evaluate the performance of FIFNN,we chose two testing functions with different complexity to be modeled,and compare with other types of *** simulation results show that the converging and the approximating ability of our proposed network are better than the other IFNNs' models in some situations.

关键词： Interval feed-forward neural network bp learning algorithm Interval-valued data

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bp Neural Network learning algorithm and Its Software Implementation

BP Neural Network Learning Algorithm and Its Software Implem...

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International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Lin, Yingjian Chen, Xiaoji Bohai Univ Teaching & Res Inst Coll Comp Jinzhou Peoples R China

ISBN: (纸本)9783038350125

bp neural network in character recognition, pattern classification, text and voice conversion, image compression, decision support and so on aspects has the widespread application, in view of the problems existing in the actual application, this paper researches learning algorithm and software implementation. learning algorithm studies include three aspects, illustrates the basic thoughts of the bp algorithm, designed the three layers bp network structure, the mathematical model for the accurate description of algorithm. Software implementation studies include two aspects, the network model of all neurons become linked list structure and storage structure is designed, the design of the software process and will implement the process into four steps. bp algorithm of the software implementation is a basic work for the application of bp neural network, using the research results of this paper, the user can easily neural network design and simulation.

关键词： bp neural network bp learning algorithm software implementation data structure

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Refracture candidate selection using hybrid simulation with neural network and data analysis techniques

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2014年 123卷 138-146页

作者： Wang Yanfang Salehi, Saeed Univ Louisiana Lafayette Lafayette LA 70504 USA

By now very few analytical models have been developed to select well refracture candidates due to complicated multi-parameter relationships. In this study, we proposed a new method by merging mathematical data analysis with feed forward back propagation neural network utilizing post-fracturing data. The model preference is thereby based on the correlation coefficients of several selected independent variables against production performance. The solution to this expense is a tool that can identify restimulation candidates quickly and economically. We employ two mathematical analysis techniques to filter several independent yet influential parameters as inputs. These parameters are supposed to be primary factors with high impact on potential production improvement. Then we use these well data to train an artificial neural network (ANN) to predict post-fracture production. The errors of the best samples should decrease consistently along with the training samples. A minimal error of the training sets is not necessary because over-fitting of the network could be memorizing rather than generalizing. The testing results showed that there is higher than 80% prediction accuracy, which is good enough for decision making. This methodology gives credible prediction results when it is applied in Zhongyuan oilfield and provides the operators with useful recommendations to make decisions for restimulation. (C) 2014 Elsevier B.V. All rights reserved.

关键词： neural network bp learning algorithm data mining refracture candidate selection

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A Universal Adaptive Neural Control Scheme for Nonlinear Uncertain System

A Universal Adaptive Neural Control Scheme for Nonlinear Unc...

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International Conference on Mechatronics and Information Technology (ICMIT 2013)

作者： Ren, Huaiqing Liang, Yanchun Shi, Xiaohu Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)9783037859414

The traditional bp algorithm is modified and a kind of novel model reference adaptive control (MRAC) scheme is proposed based on generalized bp (Gbp) algorithm. The convergence of the control scheme is also analyzed according to Lyapunov theory. To test its effectiveness, the proposed model is used to control a nonlinear uncertain system. Simulation results indicate that the controller is effective in controlling a general class of nonlinear systems.

关键词： Adaptive neural control nonlinear system bp learning algorithm closed-loop control

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Detection and classification of flaws in concrete structure using bispectra and neural networks

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NDT & E INTERNATIONAL 2002年第1期35卷 19-27页

作者： Xiang, Y Tso, SK Wuhan Univ Technol Coll Power & Environm Engn Wuhan 430063 Peoples R China City Univ Hong Kong Ctr Intelligence Design Automat & Mfg Kowloon Hong Kong Peoples R China

The problem addressed in this paper is the detection and classification of flaws in concrete structure. It is known that higher-order spectra contain information not present in the power spectrum and can suppress Gaussian noise. Thus estimates of higher-order spectra have been shown to be useful in certain signal processing problems. This paper is concerned with the feature extraction from bispectra for concrete flaw detection. Impact-echo experiments are carried out for three different types of flaw in concrete structure. For each monitoring signal, after bispectral estimation, features are selected from the modules of bispectra in the primary region. For automatic interpretation, a multilayer back-propagation neural network is used as a classifier. Both clean data and data with additive white Gaussian noise are used for training and testing. The classification results obtained experimentally demonstrate that this method has good detection rates in varying environments. (C) 2001 Published by Elsevier Science Ltd.

关键词： concrete structure flaw detection bispectrum neural networks bp learning algorithm impact-echo test

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A Modified algorithm for Feedforward Neural Networks

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Journal of China University of Mining and Technology 2002年第1期12卷 103-107页

作者：夏战国管红杰李政伟孟斌 Department of Computer Science & Technology CUMT Xuzhou Jiangsu 221008 P.R.China

As a most popular learning algorithm for the feedforward neural networks, the classic bp algorithm has its many shortages. To overcome some of the shortages, a modified learning algorithm is proposed in the article. A... 详细信息

关键词： feedforward neural networks bp learning algorithm network complexity learning step size

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Deriving a transient stability index by neural networks for power-system security assessment

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 1998年第6期11卷 771-779页

作者： Tso, SK Gu, XP Zeng, QY Lo, KL City Univ Hong Kong Ctr Intelligent Design Automat & Mfg Kowloon Peoples R China Elect Power Res Inst Beijing 100085 Peoples R China Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland

This paper proposes an approach for establishing a transient stability classifier and derives a continuous transient stability index, using a three-layer feed-forward artificial neural network (ANN), for on-line security assessment in large power systems. With the derived stability index, a never classification scheme creating an 'indeterminate' class, is introduced to minimize misclassifications and to improve the reliability of the classification results. Several post-fault abstract attributes about the system generators' acceleration rates and kinetic energies provide the basis for the stability classification. In order to derive the transient stability index, a semi-supervised backpropagation (bp) learning algorithm, making use of a specially defined error function, is developed. The proposed approach can not only distinguish whether a power system is stable or unstable, on the basis of the specific post-fault attributes, but can also provide a relative stability quantifier. Furthermore, as the number of the selected abstract attributes is independent of the system size, the methodology of the proposed approach can realistically be applied to large power systems. The 10-unit 39-bus New England power system is employed to demonstrate the proposed approach. The numerical results show that the ANN-based classifier can assess the transient stability reasonably well. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： neural networks stability index transient stability security assessment bp learning algorithm pattern classification power systems

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