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 e-book is a part of a 3 quantity set that constitutes the refereed court cases 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 provided have been rigorously reviewed and chosen from a complete of 1,975 submissions. The papers are prepared 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 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 acceptance, 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 functions 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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Choose the coupling matrix G and the linking matrix D as ⎡ ⎤ −3 1 2 40 G = ⎣ 1 −2 1 ⎦ , D= . 3493). 2, where e(t) = (e1 (t), e2 (t))T and ej (t) = 3 (xij (t)−x1j (t))2 i=2 and the initial stats for (15) are taken randomly constants in [0, 1] × [0, 1]. 2 confirm that the dynamical system (15) is globally exponentially synchronized. References 1. : Neurons with Graded Response Have Collective Computational Properties Like Those of Two-Stage Neurons. Proc. Natl. Acad. Sci. USA 81 (1984) 3088-3092 2.

Therefore, the approaches developed here further extend the ideas and techniques presented in recent literature, and they are also simple to implement in practice. Example 1. 1 . 5 where the synchronization state of the coupled delayed neural network (16) is 100 1 defined as s(t) = xk (t). 100 k=1 It should be noted that the isolate neural network x(t) ˙ = −Cx(t) + Af (x(t)) + Aτ g(x(t − 1)), (17) is actually a chaotic delayed Hopfield neural network [8], [9] (see Fig. 1 (a)). 22 L. Xiang, J. Zhou, and Z.

Based on impulsive control theory on delayed dynamical systems, a simple yet less conservative criteria is derived for robust impulsive synchronization of coupled delayed neural networks. It is shown that the approaches developed here further extend the ideas and techniques presented in recent literature, and they are also simple to implement in practice. Finally, a typical scale-free (SF) network composing of the representative chaotic delayed Hopfield neural network nodes is used as an example to illustrate this impulsive control scheme, and the numerical simulations also demonstrate the effectiveness and feasibility of the proposed control techniques.

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