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Artificial neural network power system stabiliser trained with an improved bp algorithm

IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION 1996年第2期143卷 135-141页

作者： Guan, L Cheng, S Zhou, R NANYANG TECHNOL UNIV SCH ELECT & ELECTR ENGNSINGAPORE 2263SINGAPORE

The paper presents an artificial neural network (ANN) power system stabiliser (NNPSS). The neural network in the proposed NNPSS is trained by an improved bp algorithm. The main difference between the proposed bp algorithm and the conventional bp algorithm is that two variable factors, a learning rate factor epsilon and a momentum factor alpha, are used. This significantly improves the convergence of the ANN's training. A four layer (7-7-4-1) ANN is used to design the NNPSS. The NNPSS is trained by samples obtained from power systems controlled by nonlinear power system stabilisers. The ability of the trained NNPSS to handle unknown disturbances using measurable variables has been investigated in two power systems, a single machine to infinite bus power system and a three machine power system. Test results show that the NNPSS is effective in damping out power system oscillations and is robust to the variations of both the system parameters and the system operating conditions.

关键词： intelligent control bp algorithm power system stabiliser

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A New Correction Method of Electronic-weighing System for Blast Furnace

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Journal of Iron and Steel Research International 1998年第2期5卷 34-37页

作者： Zou Tiepeng Xia Jingbo Wang Shi Lu Zengxi Northeastern University

In the injection of pulverized coal into a blast furnace, there are some factors which affect the readout of electronic weighing system. Through analyzing the measuring errors, it is found that the main reasons are pressure fluctuations of storage tank and puffing tank. According to the interaction of pressures, a neural network based method combined with fuzzy logic is adopted to enhance the precision. Experimental results show this method is satisfactory.

关键词： neural network fuzzy logic bp algorithm electronic weighing system

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Neural network system identification of chaotic optical systems with the chaos speedup, bp algorithm

Neural network system identification of chaotic optical syst...

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Conference on Functional Photonic and Fiber Devices

作者： Weng, ZH Yang, HJ Shen, K Zhou, LW ACAD SINICA CHUNGCHUN INST OPT & FINE MECHSTATE KEY LAB APPL OPTCHANGCHUN 130022PEOPLES R CHINA

ISBN: (纸本)0819420697

An algorithm for quickly training the bp neural network system identifier (bpNNSI) of the chaotic optical systems is presented in this paper. The ability of this algorithm, termed as the chaos speedup bp algorithm (CSbpA), has been demonstrated with the computer simulation of identifying the Bragg diffraction acousto-optic system (BDAOS) in which a 1:4:1 bp network was employed in identification. Taking the normalized output time series of the BDAOS as the training series, the bpNNSI was trained with the CSbpA as follows: (1) trained the bpNNSI to learn a chaotic state of the BDAOS with the bp algorithm where the initial weight distribution was set randomly; (2) took the final weight distribution obtained in (1) as the initial weight distribution for the other states of the BDAOS to be identified; (3) trained the bpNNSI to learn the other states still with the bp algorithm but with the initial weight distribution obtained in (2).

关键词： system identification bp network bp algorithm chaotic optical systems adaptive control

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Reliability Management and Comprehensive Evaluation Method on Telecommunication Networks

Reliability Management and Comprehensive Evaluation Method o...

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1998 International Conference on Communication Technology

作者： Ding Kaisheng Liang Xiongjian Management Engineering Department Beijing University of Posts & Telecommunications

ISBN: (纸本)7800908275

The reliability of telecommunication networks is one of the most important quality *** this paper,based on the analysis of indicator system of telecommunication network reliability,a model is provided to evaluate the growth of telecommunication network reliability. In the evaluating model,how to determine the weight of every reliability indicator is a key *** combine the principal component analysis method with neural network method to determine the weight of every ***,an example of application of the evaluation model in a local network is given.

关键词： Reliability Management Reliability Indicator Evaluation Method bp algorithm

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Segmenting the color image in a simple background by ANN method

Segmenting the color image in a simple background by ANN met...

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International Symposium on Multispectral Image Processing and Pattern Recognition

作者： Xiong, LH Li, DH Hu, HP Jin, G Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence State Educ Commiss Lab Image Informat Proc & Inte Wuhan 430074 Peoples R China

ISBN: (纸本)0819430064

The neural network method applied for the color image segmentation of the human's head image in the simple background is studied in this paper. The adopted network model is bp network, The image segmented by the region growing method is used as the training Bet and the bp algorithm is adopted to train the target and background image. Then segmenting testing image can be processed. Experimenting results show that the segmenting effect of this method is as good as the region growing method.

关键词： color image segmentation artificial neural network bp algorithm

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Nonlinear stretching for enhanced learning of the backpropagation algorithm in target recognition

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Microwave and Optical Technology Letters 1998年第6期16卷

作者： Yi Ge Soyoung S. Cha Jiajun Zhang Li Zhang Anzhi He Department of Mechanical Engineering University of Illinois at Chicago Chicago Illinois 60607 Department of Applied Physics Nanjing University of Science and Technology Nanjing P.R. China

In spite of its relatively slow learning speed, backpropagation (bp) is one of the most popular neural network training algorithms. Here, a method based on nonlinear stretching is presented that modifies the activation function in a bp algorithm to speed up the convergence in training. A target recognition system that incorporates the approach and moment invariants is formulated and tested. The test results indicate that the speed of convergence can be effectively increased through appropriate selection of a stretch factor. © 1997 John Wiley & Sons, Inc. Microwave Opt Technol Lett 16: 334–337, 1997.

关键词： target recognition bp algorithm nonlinear stretching moment invariants

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Escape from the slowdown area on training of back-propagation algorithm by using a jumping method

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Systems and Computers in Japan 1998年第4期28卷

作者： Kohtaro Yamada Narade Pecharanin Akira Taguchi Nobukazu Iijima Mototaka Sone Faculty of Engineering Musashi Institute of Technology Tokyo Japan 158

The typical error back-propagation learning algorithm (bp) of neural networks with layered structure is shown to be useful in a wide range of practical applications. The “slowdown” of the change in the evaluation error function that occurs in the course of learning, however, has a large effect on the training period and on convergence. Slowdown seems to arise in the area where the partial derivative of the evaluation error function with respect to the network coefficient is very small. To avoid slowdown of training in the bp algorithm, this paper proposes to speed up training via jumping correction of the network coefficient. More precisely, the change of the network coefficient as a function of the number of training cycles is locally approximated by a second-order function and the coefficient is updated by jumping of that function. It is verified that slowdown in the bp algorithm can be avoided by applying the proposed method and that training converges at a high speed. The proposed method is compared with the existing prediction method, which is a speed-up technique for training, and the proposed method is shown to reduce training time to between one-third and one-half. © 1997 Scripta Technica, Inc. Syst Comp Jpn, 28(4): 48–57, 1997

关键词： Neural network bp algorithm training slowdown network coefficient update jumping algorithm

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Statistical analysis of a two-layer backpropagation algorithm used for modeling nonlinear memoryless channels: The single neuron case

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1997年第3期45卷 747-756页

作者： Bershad, NJ Ibnkahla, M Castanie, F INST NATL POLYTECH TOULOUSE ENSEEIHT GAPSEF-31077 TOULOUSEFRANCE

Neural networks have been used for modeling the nonlinear characteristics of memoryless nonlinear channels using backpropagation (bp) learning with experimental training data. In order to better understand this neural network application, this paper studies the transient and convergence properties of a simplified two-layer neural network that uses the bp algorithm and is trained with zero mean Gaussian data. The paper studies the effects of the neural net structure, weights, initial conditions, and algorithm step size on the mean square error (MSE) of the neural net approximation. The performance analysis is based on the derivation of recursions for the mean weight update that can be used to predict the weights and the MSE over time. Monte Carte simulations display good to excellent agreement between the actual behavior and the predictions of the theoretical model.

关键词： .initial conditions Statistical Analysis Backpropagation recursions bp algorithm TWT Used for Modeling Analysis of Two-Layer Single Neuron two-layer neural network

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Nonlinear stretching for enhanced learning of the backpropagation algorithm in target recognition

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 1997年第6期16卷 334-337页

作者： Ge, Y Cha, SYS Zhang, JJ Zhang, L He, AZ Department of Mechanical Engineering University of Illinois at Chicago Chicago Illinois 60607 Department of Applied Physics Nanjing University of Science and Technology Nanjing P.R. China

In spite of its relatively slow learning speed, backpropagation (bp) is one of the most popular neutral network training algorithms. Here, a method based on nonlinear stretching is presented that modifies the activation function in a bp algorithm to speed up the convergence in training. A target recognition system that incorporates the approach and moment invariants is formulated and tested. The test results indicate that the speed of convergence can be effectively increased through appropriate selection of a stretch factor. (C) 1997 John Wiley & Sons, Inc.

关键词： target recognition bp algorithm nonlinear stretching moment invariants

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Study on the model and algorithm of backpropagation network

Study on the model and algorithm of backpropagation network

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2nd International Symposium on Test and Measurement (ISTM/97)

作者： Liu, WY Meng, ZH Machinery Engineering Coll Hebei China

ISBN: (纸本)7800033910

The activation function for the node of Backpropagation (bp) network is the Sigmoid function. The gain of the Sigmoid is usually set equal to 1. Author considers that the fixative gain of the Sigmoid is disadvantageous to the bp network to simulate the function of brain cells, and in some range it limits the convergent rate of bp network while training. Therefore, the paper presents a new improved bp network which the gain of the Sigmoid is variable while training. The improved bp network not only simulates brain cells better, but also converges faster while training.

关键词： neural network bp network bp algorithm

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