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 lawsuits of the sixth overseas Symposium on Neural Networks, ISNN 2009, held in Wuhan, China in might 2009.
The 409 revised papers awarded have been rigorously reviewed and chosen from a complete of 1.235 submissions. The papers are equipped in 20 topical sections on theoretical research, balance, time-delay neural networks, laptop studying, neural modeling, determination making platforms, fuzzy platforms and fuzzy neural networks, help vector machines and kernel tools, genetic algorithms, clustering and category, development acceptance, clever regulate, optimization, robotics, photograph processing, sign processing, biomedical purposes, 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
Acknowledgment This work is supported by Ministry of Higher Education (MOHE) under Fundamental Research Grant Scheme (Vote No. 78243). Authors would like to thank Research Management Centre (RMC) Universiti Teknologi Malaysia, for the research activities and Soft Computing Research Group (SCRG) for the support and incisive comments in making this study a success. References 1. : Radial Basis Function Networks for Classifying Process Faults. Control Systems Magazine 11(3), 31–38 (1991) 2. : Training Radial Basis Function Networks with Particle Swarms.
The global crowding algorithm is detailed as follows: Procedure Global Crowding (Input: A) Output: C; size=|A|; for i = 1 to size for j = 1 to size D[i][j] = |A[i] –A[j]|; end for j; Diversity Maintenance Strategy Based on Global Crowding 15 Sort(D[i]); t = 0; for j = 2 to size k = j – 1; t = t + g(k)*D[i][j]; end for j; C[i] = t ; end for i; end Procedure 4 Numerical Experimentation In the process of constructing multi-objective evolutionary algorithm, most of algorithms use the strategy of randomly initializing population.
Proof. By Lemma 1 and Lemma 2, we know that the neural network (6) has the solution x(t) with initial point x(t0 ) and unique equilibrium point x∗ . Let x(t) is any solution of the network (1) with any initial function x(t0 ), and x∗ denotes the equilibrium point of the neural network (6) By (6), we get x(t) = e−I(t−t0 ) x0 + t t0 e−I(t−s) [PΩ (−α∇f (PΩ (x(s) − α∇f (x(s)))) +PΩ (x(s) − α∇f (x(s))))]ds, (8) and t x∗ = e−I(t−t0 ) x∗ + t0 e−I(t−s) [PΩ (−α∇f (PΩ (x∗ − α∇f (x∗ ))) +PΩ (x∗ − α∇f (x∗ )))]ds.
Advances in Neural Networks – ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part III by Minghui Jiang, Yongqing Zhao, Yi Shen (auth.), Wen Yu, Haibo He, Nian Zhang (eds.)