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

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

ISBN-10: 3540723927

ISBN-13: 9783540723929

This booklet 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 offered have been conscientiously reviewed and chosen from a complete of 1,975 submissions. The papers are equipped in topical sections on neural fuzzy regulate, neural networks for keep an eye on functions, adaptive dynamic programming and reinforcement studying, neural networks for nonlinear platforms modeling, robotics, balance research of neural networks, studying and approximation, information mining and have extraction, chaos and synchronization, neural fuzzy platforms, education and studying algorithms for neural networks, neural community constructions, neural networks for development attractiveness, SOMs, ICA/PCA, biomedical purposes, feedforward neural networks, recurrent neural networks, neural networks for optimization, help vector machines, fault diagnosis/detection, communications and sign processing, image/video processing, and purposes of neural networks.

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Additional resources for Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part II

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20 L. Xiang, J. Zhou, and Z. Liu (A3 ) Let μ > 0 satisfy μ − p + qeμτ ≤ 0, and θk = max 1, (1 + dk )2 , ln θk tk − tk−1 θ = sup k∈Z + (9) such that θ < μ. Then the controlled coupled delayed neural network (2) is robustly exponentially synchronized. Brief Proof. Let vi (t) = xi (t) − s(t) (i = 1, 2, · · · , N ), then the error dynamical system can be rewritten as ⎧ τ ˜ ˜(vi (t − τ )) i (t) + Af (vi (t)) + A g ⎪ ⎪ v˙ i (t) = −Cv ⎪ N ⎨ + bij Γ vj (t) + J, t = tk , t ≥ t0 , (10) ⎪ ⎪ j=1 ⎪ ⎩ vi (tk ) = (1 + dk )vi (t− t = tk , k = 1, 2, · · · , k ), ˜ where f (vi (t)) = f (vi (t) + s(t)) − f (s(t)), g˜(vi (t − τ )) = g(vi (t − τ ) + s(t − τ )) − g(s(t−τ )) and J = Af (s(t))+Aτ g(s(t−τ ))+ 1 N N [Af (xk (t))+Aτ g(xk (t−τ ))].

26 T. Huang and C. Li In this paper, we assume that H: fi is a bounded function defined on R and satisfies |fi (x) − fi (y)| ≤ li |x − y|, i = 1, · · · , n. (4) for any x, y ∈ R. In the following, we cite several concepts on quasi-synchronization and impulsive differential equation. Definition 1. ([9]). Let χ denote a region of interest in the phase space that contains the chaotic attractor of system (1). The synchronization schemes (1) and (2) are said to be uniformly quasi-synchronized with error bound ε > 0 if there exists a T ≥ t0 such that ||x(t) − y(t)|| ≤ ε for all t ≥ T starting from any initial values x(t0 ) ∈ χ and y(t0 ) ∈ χ.

Bifurcation Chaos, 4 (1994) 979-989 32 T. Huang and C. Li 19. : Adaptive Exponential Synchronization of Delayed Chaotic Networks. Physica A 370 (2006) 832-842 20. : Fuzzy Cellular Neural Networks: Theory. In Proc. of IEEE International Workshop on Cellular Neural Networks and Applications, (1996)181-186 21. Yang T. W. : Fuzzy Cellular Neural Networks: Applications. In Proc. of IEEE International Workshop on Cellular Neural Networks and Applications, (1996)225-230. 22. : The Global Stability of Fuzzy Cellular Neural Network.

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