Advances in Neural Networks – ISNN 2007: 4th International by Hongwei Wang, Hong Gu (auth.), Derong Liu, Shumin Fei,

By Hongwei Wang, Hong Gu (auth.), Derong Liu, Shumin Fei, Zengguang Hou, Huaguang Zhang, Changyin Sun (eds.)

This publication is a part of a 3 quantity set that constitutes the refereed complaints 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 rigorously reviewed and chosen from a complete of 1,975 submissions. The papers are equipped in topical sections on neural fuzzy keep watch over, neural networks for keep an eye on functions, adaptive dynamic programming and reinforcement studying, neural networks for nonlinear structures modeling, robotics, balance research of neural networks, studying and approximation, information mining and have extraction, chaos and synchronization, neural fuzzy structures, education and studying algorithms for neural networks, neural community constructions, neural networks for trend reputation, SOMs, ICA/PCA, biomedical functions, 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.

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Let χ = {x ∈ Rn |||x|| ≤ δ1 }, and parameter mismatches satisfy ΔDT ΔD + ΔAT ΔA + ΔB T ΔB ≤ δ2 , δ = δ12 δ22 . and let the sequence of impulses be equidistant and separated by an interval τ . If there exists a symmetric and positive definite matrix P > 0 such that the following conditions hold: (i)−2P D + P 2 + 2L|A| + P 2 L2 + 2L|B| + P 2 L2 − λP ≤ 0, (ii) (I + C)T P (I + C) − ρP ≤ 0, (iii) lnρ + λτ < 0. then the synchronization error system (3) converges exponentially to a small region containing the origin which is τδ } λm (P )ρ(lnρ + τ λ) {e ∈ Rn |||e|| ≤ Thus, the quasi-synchronization with error bound ε = τδ λm (P )ρ(lnρ+τ λ) (11) between the systems (1) and (2) achieved.

8–15, 2007. © Springer-Verlag Berlin Heidelberg 2007 Chaos Synchronization Between Unified Chaotic System and Genesio System 9 2 Systems Description and Mathematical Models Consider nonlinear chaotic system as follows. { x = f (t , x) y = g (t , y ) + u (t , x, y ) (1) where x, y ∈ R n , f , g ∈ R × R n are differentiable functions, the first equation in (1) is the drive system, and the second one is the response system, u (t , x, y ) is the control input. Let e = y − x be the synchronization error, our goal is to design a controller u such that the trajectory of response system with initial conditions y0 asymptotically approaches the drive system with initial conditions x0 and finally implements synchronization, in the sense that lim e = lim y (t , y0 ) − x(t , x0 ) = 0 t →∞ t →∞ where || ⋅ || is the Euclidean norm.

Acta Physica Sinica 50 (2002) 847-850 7. : Stability of Observer-Based Chaotic Communications for a Class of Lure Systems. Bifurcation and Chaos 12 (2002) 1605-1618 8. : Nonlinear Observer Design to Synchronize Hyperchaotic Systems via a Scalar Signal. IEEE Transaction of Circuits Systems 44 (1997) 1011-1014 9. : An LMI Criterion for Chaos Synchronization via the LinearState-Feedback Approach. IEEE International Symposium Computer Aided Control System Design (2004) 368-371 10. : Nonlinear Control Systems.

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