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.)

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 may possibly 2009.

The 409 revised papers provided have been conscientiously 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, computing device studying, neural modeling, determination making platforms, fuzzy platforms and fuzzy neural networks, aid vector machines and kernel equipment, genetic algorithms, clustering and class, trend popularity, clever keep watch over, 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 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 particles are evaluated using a fitness function to seek the optimal solution. Particles values of RBF parameters are then initialized with values which are obtained from the k-means algorithm while particles values of the weights and bias are initialized randomly or from LMS algorithm. The particles are updated accordingly using the equation (2) and (3). The procedure for implementing PSO global version (gbest) is shown in Figure 1. Optimization of RBF network parameters with PSO, the fitness value of each particle is the value of the error function evaluated at the current position of the particle and position vector of the particle corresponds to the parameters matrix of the network.

ISNN 2009, Part III, LNCS 5553, pp. 10–18, 2009. © Springer-Verlag Berlin Heidelberg 2009 Diversity Maintenance Strategy Based on Global Crowding 11 order to avoid the convergence of population to single individual during the evolutionary process, corresponding strategies must be designed to maintain the diversity of population. Researchers have conceived several different strategies through which algorithm can finally find wide-spread and uniform distributed solutions. Among of these researches, NSGA-II [2], SPEA-II [3] and PAES [4] have made significant contributions to multi-objective evolutionary optimization.

37, 384–397 (1990) 4. : Two Metric Projection Methods for Constraints Optimization. SIAM J. Contr. Optim. 22, 936–964 (1984) 5. : Global Exponential Stability of Recurrent Neural Networks for Solving Optimization and Related Problems. IEEE Trans. Neural Networks 4, 1017–1022 (2000) 6. : An Improved Neural Network for Convex Quadratic Optimization with Application to Real-time Beamforming. Neurocomputing 64, 359–374 (2005) 7. : A Recurrent Neural Network for Solving Linear Projection Equations. Neural Network A 13, 337–350 (2000) 8.

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