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 may perhaps 2009.
The 409 revised papers provided 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, computing device studying, neural modeling, determination making platforms, fuzzy platforms and fuzzy neural networks, help vector machines and kernel tools, genetic algorithms, clustering and class, development reputation, clever regulate, optimization, robotics, photo processing, sign processing, biomedical functions, fault analysis, telecommunication, sensor community and transportation structures, in addition to applications.
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Additional resources for Advances in Neural Networks – ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part III
LNCS, vol. 4233, pp. 1148–1155. Springer, Heidelberg (2006) 4. : Document Clustering Using Particle Swarm Optimization. In: Proceedings of Swarm Intelligence Symposium, 2005. SIS 2005, pp. 185–191. IEEE, Los Alamitos (2005) 5. : Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, IV, Piscataway, NJ, pp. 1942–1948 (1995) 6. : A Modified Particle Swarm. In: Proceedings of 1998 IEEE International Conference on Evolutionary Computation, Piscataway, NJ, pp.
The second stage involves weights establishment by connecting the hidden layer with the output layer. This is determined by Singular Value Decomposition (SVD) or Least Mean Squares (LMS) algorithms . The problem of selecting the appropriate number of basis functions remains a critical issue for RBF W. Yu, H. He, and N. ): ISNN 2009, Part III, LNCS 5553, pp. 19–29, 2009. N. M. Shamsuddin Networks. The number of basis functions controls the complexity and the generalization ability of RBF Networks.
The operation result shows the feasibility and high efficiency of the proposed algorithm, while compared with tradition method, CCPSO is faster and has the higher precision. Therefore a new method is proposed. Keywords: Chaos, Cultural algorithm, Particle Swarm Optimization, Logistic map. 1 Introduction Particle swarm optimization (PSO) is a new global optimal evolution algorithm, which was proposed by Kennedy and Eberhart in 1995. It is inspired by the observations of the social behavior of animals, such as bird flocking, fish schooling and swarm theory .
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.)