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

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 overseas Symposium on Neural Networks, ISNN 2009, held in Wuhan, China in may perhaps 2009.

The 409 revised papers offered 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, computer studying, neural modeling, selection making platforms, fuzzy structures and fuzzy neural networks, aid vector machines and kernel equipment, genetic algorithms, clustering and type, trend acceptance, clever regulate, optimization, robotics, snapshot processing, sign processing, biomedical purposes, fault prognosis, telecommunication, sensor community and transportation structures, in addition to applications.

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Extra 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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The method performs feature selection and RBF training simultaneously. PSO algorithm has been introduced [2] to train RBF Network related to automatic configuration of network architecture related to centers of RBF. Two training algorithm were compared. One was PSO algorithm. The other was newrb routine that was included in Matlab neural networks toolbox as standard training algorithm for RBF network. Hybrid Learning Enhancement of RBF Network Based on Particle Swarm Optimization 21 A hybrid PSO (HPSO) was proposed [11] with simulated annealing and Chaos search technique to train RBF Network.

LNCS, vol. 3971, pp. 577–583. Springer, Heidelberg (2006) 12. : Training RBF Neural Network via Quantum-Behaved Particle Swarm Optimization. , Wang, D. ) ICONIP 2006. LNCS, vol. 4233, pp. 1156–1163. Springer, Heidelberg (2006) 13. : Hybrid Recursive Particle Swarm Optimization Learning Algorithm in the Design of Radial Basis Function Networks. Journal of Marine Science and Technology 15, 31–40 (2007) 14. : Multivariable Functional Interpolation and Adaptive Networks. cn Abstract. A new version of the classical particle swarm optimization (PSO), namely, Chaos culture particle swam optimization (CCPSO), is proposed to overcome the shortcoming of the premature of the classical PSO.

005 or maximum iteration of 10000. 005 or maximum iteration of 20000 has been achieved. Table 4. 77 Fig. 3. Convergence of Cancer dataset In Cancer learning process, from Table 4 shows PSO-RBFN takes 10000 iterations compared to 20000 iterations in BP-RBFN to converge. PSO-RBFN managed to converge at iteration 10000, while BP-RBFN converges at a maximum iteration of 20000 and illustrates that PSO-RBFN is better than BP-RBFN. Figure 3 shows PSORBFN significantly reduce the error with small number of iterations.

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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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