By Hongwei Wang, Hong Gu (auth.), Derong Liu, Shumin Fei, Zengguang Hou, Huaguang Zhang, Changyin Sun (eds.)
This ebook is a part of a 3 quantity set that constitutes the refereed lawsuits of the 4th foreign Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007.
The 262 revised lengthy papers and 192 revised brief papers offered have been rigorously reviewed and chosen from a complete of 1,975 submissions. The papers are equipped 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 platforms 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 development acceptance, 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 functions 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
Int J Bifur Chaos. R. R. R. com 1 2 Abstract. The present paper studies robust impulsive synchronization of coupled delayed neural networks. Based on impulsive control theory on dynamical systems, a simple yet less conservative criteria ensuring robust impulsive synchronization of coupled delayed neural networks is established. Furthermore, the theoretical result is applied to a typical scale-free (SF) network composing of the representative chaotic delayed Hopﬁeld neural network nodes, and numerical results are presented to demonstrate the eﬀectiveness of the proposed control techniques.
N . Lemma 1 . Let ⊗ denotes the notation of Kronecker product, α ∈ R, A, B, C and D are matrices with appropriate dimensions, then (1) (2) (3) (αA) ⊗ B = A ⊗ (αB); (A + B) ⊗ C = A ⊗ C + B ⊗ C; (A ⊗ B)(C ⊗ D) = (AC) ⊗ (BD). Global Synchronization in an Array of Delayed Neural Networks 2 35 Main Results In this section, Lyapunov functional method will be employed to investigate the global exponential synchronization of system (3). Theorem 1. Under the assumption (H), system (3) with initial condition (4) is globally exponentially synchronized, if there exist three positive deﬁnite matrices Pi > 0 (i = 1, 2, 3) and two positive diagonal matrices S, W such that the following LMIs are satisﬁed for all 1 ≤ i < j ≤ N : ⎡ −P1 C − CP1 + P3 LS + P1 A − N Gij P1 D ⎢ SL + AT P1 − N Gij DT P1 P2 − 2S Ωij =⎢ ⎣ 0 0 B T P1 0 ⎤ 0 P1 B ⎥ 0 0 ⎥ < 0, ⎦ −P3 LW W L −P2 − 2W (5) where L = diag(l1 , l2 , .
The most common regime of synchronization been investigated is complete synchronization, which implies the coincidence of states of interacting (master and response) systems. However, due to the parameter mismatch [2,6,7,9,18] which is unavoidable in real implementation, the master system and response system are not identical and the resulting synchronization is not exact. It is impossible to achieve complete synchronization. However, it is possible to make the synchronization error bounded by a small positive constant ε which is depended on the diﬀerences between parameters of two fuzzy neural networks.