By Hongwei Wang, Hong Gu (auth.), Derong Liu, Shumin Fei, Zengguang Hou, Huaguang Zhang, Changyin Sun (eds.)
This booklet is a part of a 3 quantity set that constitutes the refereed lawsuits of the 4th overseas Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007.
The 262 revised lengthy papers and 192 revised brief papers awarded have been conscientiously reviewed and chosen from a complete of 1,975 submissions. The papers are geared up in topical sections on neural fuzzy keep an eye on, neural networks for regulate purposes, adaptive dynamic programming and reinforcement studying, neural networks for nonlinear structures modeling, robotics, balance research of neural networks, studying and approximation, facts mining and have extraction, chaos and synchronization, neural fuzzy platforms, education and studying algorithms for neural networks, neural community constructions, neural networks for trend reputation, SOMs, ICA/PCA, biomedical purposes, feedforward neural networks, recurrent neural networks, neural networks for optimization, aid vector machines, fault diagnosis/detection, communications and sign processing, image/video processing, and purposes of neural networks.
Read or Download Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part II PDF
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Additional info for Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part II
Synchronization in General Complex Delayed Dynamical Networks. IEEE Trans. CAS-I 53 (2006) 733-744 12. : Stability Analysis of Delay Neural Networks with Impulsive Eﬀects. IEEE Trans. CAS-II 52 (2005) 517-521. 13. : Impulsive Control Theory. O. cn 1 2 Abstract. In this paper, we study the eﬀect of parameter mismatches on the fuzzy neural networks with impulses. Since it is impossible to make two non-identical neural networks complete synchronized, we study the synchronization of two neural networks in terms of quasi-synchronization.
043901. 15. : New Approach to Synchronization Analysis of Linearly Coupled Ordinary Diﬀerential Systems. Physica D 213 (2006) 214-230 16. : Synchronization in Chaotic systems. Physical Review Letters 64 (1990) 821-824. 17. : Synchronization in An Array of Linearly Coupled Networks with Time-varying Delay. Physica A, 366(2006) 197-211 18. Wu,C. : A Uniﬁed Framework for Synchronization and Control of Dynamical Systems. Int. J. Bifurcation Chaos, 4 (1994) 979-989 32 T. Huang and C. Li 19. : Adaptive Exponential Synchronization of Delayed Chaotic Networks.
Liang, P. Li, and Y. Yang above discussions, in this paper, the following nonlinearly coupled neural network model will be studied: dxi (t) dt N = −Cxi (t) + Af (xi (t)) + Bf (xi (t − τ )) + I(t) + Gij Df (xj (t)) (1) j=1 where i = 1, 2, . . , N and xi (t) = (xi1 (t), . . , xin (t))T is the state vector of the ith network at time t; C = diag(c1 , . . , cn ) > 0 denotes the rate with which the cell i resets its potential to the resting state when isolated from other cells and inputs; A and B are the weight matrix and the delayed weight matrix, respectively; activation function f (xi (t)) = (f1 (xi1 (t)), .
Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part II by Hongwei Wang, Hong Gu (auth.), Derong Liu, Shumin Fei, Zengguang Hou, Huaguang Zhang, Changyin Sun (eds.)