Advances in Neural Networks – ISNN 2009: 6th International - download pdf or read online

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

ISBN-10: 3642015123

ISBN-13: 9783642015120

ISBN-10: 3642015131

ISBN-13: 9783642015137

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

The 409 revised papers awarded have been conscientiously reviewed and chosen from a complete of 1.235 submissions. The papers are prepared in 20 topical sections on theoretical research, balance, time-delay neural networks, desktop studying, neural modeling, choice making platforms, fuzzy structures and fuzzy neural networks, help vector machines and kernel equipment, genetic algorithms, clustering and category, development popularity, clever keep an eye on, optimization, robotics, picture processing, sign processing, biomedical purposes, fault analysis, telecommunication, sensor community and transportation structures, in addition to applications.

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Extra 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

Sample text

CA has been shown to be very effective in decreasing computational cost when combined with other algorithms [3]. The framework of CA is shown in Fig. 1, which consists of three components, a population space, a belief space and a protocol with which the two spaces exchange information. Any population based algorithm can be adopted as the evolution progress of the population space which produces knowledge [3]. Then, belief space selectively accepts the knowledge, and uses the knowledge to adjust its knowledge structure, with which the belief space can guide population space evolution in the next generation.

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. RBF Networks with too few basis functions cannot fit the training data adequately due to limited flexibility. On the other hand, those with too many basis functions yield poor generalization abilities since they are too flexible and fit the noise in the training data. The methods mentioned above require designers to fix the structure of networks in advance according to prior knowledge.

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. 1945–1950 (1998) 7. : A Modified Error Function for the Backpropagation Algorithm.

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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.)

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