Download Advances in Neural Networks – ISNN 2009: 6th International by Minghui Jiang, Yongqing Zhao, Yi Shen (auth.), Wen Yu, Haibo PDF

By Minghui Jiang, Yongqing Zhao, Yi Shen (auth.), Wen Yu, Haibo He, Nian Zhang (eds.)

The 3 quantity set LNCS 5551/5552/5553 constitutes the refereed court cases of the sixth foreign Symposium on Neural Networks, ISNN 2009, held in Wuhan, China in might 2009.

The 409 revised papers offered have been rigorously reviewed and chosen from a complete of 1.235 submissions. The papers are geared up in 20 topical sections on theoretical research, balance, time-delay neural networks, computer studying, neural modeling, choice making structures, fuzzy structures and fuzzy neural networks, aid vector machines and kernel tools, genetic algorithms, clustering and type, development acceptance, clever regulate, optimization, robotics, picture processing, sign processing, biomedical functions, fault analysis, telecommunication, sensor community and transportation structures, in addition to applications.

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Additional info for Advances in Neural Networks – ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part III

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In: Proceedings of the Congress on Evolutionary Computation, pp. com Abstract. This study proposes RBF Network hybrid learning with Particle Swarm Optimization for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths.

The convergence proof of the neural network (14) is similar to one of Theorem 1 and here omitted. 4 Examples In this section, two examples will be given to show the validity of our results. Example 1. } (16) A Modified Projection Neural Network 7 8 6 4 2 X x3 x2 0 x1 −2 −4 −6 0 1 2 3 4 5 6 Time (second) 7 8 9 10 Fig. 1. 6786 < 1. Thus, the condition of Theorem 1 holds. The modified projection neural networks (6) and (14) converges globally exponentially to the solution (0, 0, 1) of the linear variational inequality (15).

For the population vector set A, if ai ∈ A, i = 1, 2,… , A , it can be known from definition1 that the k-th nearest individual to ai is ai( k ) , which also corresponds to a certain individual a j in the population set A. The expression of crowding evaluation value of a j to ai is defined as follows: cij = ci( k ) = g (k )di( k ) , k = 1, 2,… A − 1 Wherein, cij denotes the influencing strength of ai by the arbitrary individual a j ∈ A . di( k ) (the distance between ai and a j ) denotes the k-th distance ascending order among all other individuals 's distance to ai .

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