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A fast learning algorithm.of neural network with tunable activation function

Science in China(Series F) 2004年第1期47卷 126-136页

作者： SHENYanjun WANGBingwen DepartmentofControlScienceandEngineering UniversityofScienceandTechnologyWuhan430074China

This paper presents a modified structure of a neural network with tunable activation function and provides a new learning algorithm.for the neural network training. Simulation results of XOR problem, Feigenbaum function, and Henon map show that the new algorithm.has better performance than BP (back propagation) algorithm.in terms of shorter convergence time and higher convergence accuracy. Further modifications of the structure of the neural network with the faster learning algorithm.demonstrate simpler structure with even faster convergence speed and better convergence accuracy.

关键词： neural networks RLS algorithm TAF neural model learning algorithm.

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Path Planning Approach in Unknown Environment

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International Journal of Automation and computing 2010年第3期7卷 310-316页

作者： Ting-Kai Wang Quan Dang Pei-Yuan Pan Faculty of Computing London Metropolitan University London N7 8DB UK

This paper presents a new algorithm.of path planning for mobile robots, which utilises the characteristics of the obstacle border and fuzzy logical reasoning. The environment topology or working space is described by the time-variable grid method that can be further described by the moving obstacles and the variation of path safety. Based on the algorithm. a new path planning approach for mobile robots in an unknown environment has been developed. The path planning approach can let a mobile robot find a safe path from the current position to the goal based on a sensor system. The two types of machine learning： advancing learning and exploitation learning or trial learning are explored, and both are applied to the learning of mobile robot path planning algorithm. Comparison with A＊ path planning approach and various simulation results are given to demonstrate the efficiency of the algorithm. This path planning approach can also be applied to computer games.

关键词： Path planning fuzzy reasoning unknown environment mobile robot learning algorithm.

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A neuron model with trainable activation function (TAF) and its MFNN supervised learning

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Science in China(Series F) 2001年第5期44卷 366-375页

作者：吴佑寿赵明生 1. Department of Electronic Engineering Tsinghua University 100084 Beijing China

This paper addresses a new kind of neuron model, which has trainable activation function (TAF) in addition to only trainable weights in the conventional M-P model. The final neuron activation function can be derived from a primitive neuron activation function by training. The BP like learning al-gorithm has been presented for MFNN constructed by neurons of TAP model. Several simulation ex-amples are given to show the network capacity and performance advantages of the new MFNN in com-parison with that of conventional sigmoid MFNN.

关键词： neuron model neural network TAF neuron model learning algorithm.

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Possibilistic fuzzy classification using neural networks

Possibilistic fuzzy classification using neural networks

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1997 IEEE International Conference on Neural Networks (ICNN 97)

作者： Ishibuchi, H Nii, M Department of Industrial Engineering Osaka Prefecture University Sakai Osaka 593 Gakuen-cho 1-1 Japan

ISBN: (纸本)0780341236

This paper examines the performance of a possibilistic fuzzy classification method where the possibility area of each class is identified by the learning of a multilayer feedforward neural network. The possibility areas of different classes may overlap one another in the pattern space. The overlapping region of the possibility areas of two classes is viewed as the fuzzy boundary between those classes. Thus our method does not always assign ail input pattern to a single class. When an input pattern is on a fuzzy boundary, a set of possible classes is indicated by the trained neural network as the classification result for that pattern. In this paper, we first illustrate our possibilistic fuzzy classification method. Next we examine its performance by computer simulations on real-world test problems. Then we discuss the relation between our method and the reject option. Finally we extend our method to the case where a rejection penalty is explicitly given in classification problems.

关键词： fuzzy classiJkation learning algorithm. Pattern classification possibility analysis reject option

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High-Performance of Power System Based upon ANFIS （Adaptive Neuro-Fuzzy Inference System） Controller

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Journal of Energy and Power Engineering 2014年第4期8卷 729-734页

作者： Yousif I. Al-Mashhadany Electrical Engineering Department College of Engineering University of AI-Anbar Al-Anbar 31001 Iraq

The proposed controller incorporates FL （fuzzy logic） algorithm.with ANN （artificial neural network）. ANFIS replaces the conventional PI controller, tuning the fuzzy inference system with a hybrid learning algorithm. A tuning method is proposed for training of the neuro-fuzzy controller. The best rule base and the best training algorithm.chosen produced high performance in the ANFIS controller. Simulation was done on Matlab Ver. 2010a. A case study was chopper-fed DC motor drive, in continuous and discrete modes. Satisfactory results show the ANFIS controller is able to control dynamic highly-nonlinear systems. Tuning it further improved the results.

关键词： ANFIS controller power system high performance learning algorithm.

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