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 court cases 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 awarded have been rigorously 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 keep an eye on purposes, adaptive dynamic programming and reinforcement studying, neural networks for nonlinear platforms modeling, robotics, balance research of neural networks, studying and approximation, info mining and have extraction, chaos and synchronization, neural fuzzy platforms, education and studying algorithms for neural networks, neural community buildings, neural networks for development popularity, 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.
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Extra 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
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. Circuits and Systems I: Fundamental Theory and Applications, 43(1996)880-883 23. : Impulsive Control Theory. Springer, Berlin, 2001.
Fig. 1. Synchronization errors e1 , e2 , e3 between unified chaotic system and Genesio system via active control 4 Adaptive Synchronization Between Unified Chaotic System and Genesio System with Unknown Parameters In order to compare with active control method, we still assume that Genesio system (2) is the drive system, and the controlled unified chaotic system (8) is the response system. ⎧ x1 = (25α + 10)( y1 − x1 ) + u1 ⎪ ⎨ y1 = (28 − 35α ) x1 + (29α − 1) y1 − x1 z1 + u2 ⎪⎩ z1 = x1 y1 − 8 +3α z1 + u3 We subtract (2) from equation (8) and yield (8) 12 X.
For the convenience, we give the matrix notations here. For A, B ∈ Rn×n , A ≤ B(A > B) means that each pair of the corresponding elements of A and B satisfy the inequality ≤ ( > ). Also, if A = (aij ), then |A| = (|aij )|. Synchronization of Impulsive Fuzzy Cellular Neural Networks 3 27 Main Results In this Section, we will obtain a suﬃcient condition for quasi-synchronization and estimate the synchronization bound at the same time using Lyapunov-like function. Before we state the main results, we state the following theorem ﬁrst.
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.)