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 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 provided have been conscientiously 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 watch over 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 platforms, education and studying algorithms for neural networks, neural community constructions, neural networks for development popularity, 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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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
CAS-I. IEEE Trans 52 (2005) 1431-1441 5. : Synchronization in an Array of Linearly Coupled Networks with Time-varying Delay. Physica A 366 (2006) 197-211 6. : Robust Impulsive Synchronization of Coupled Delayed Neural Networks with Uncertainties. Physica A 373 (2006) 261-272 7. : Adaptive Synchronization of Coupled Chaotic Systems Based on Parameters Identiﬁcation and Its Applications. Int. J. Bifur. Chaos 16 (2004) 2923-2933 8. : Robust Synchronization of Delayed Neural Networks Based on Adaptive Control and Parameters Identiﬁcation.
5000 (k ∈ Z + ). By taking kr = lr = 1 and δr = 12 (r = 1, 2), it is easy to verify that if γ1 = γ2 = 6, then all the conditions of Theorem 1 are satisﬁed. Hence, the the controlled coupled delayed neural network (16) will achieve robust impulsive synchronization. The simulation results corresponding to this situation are shown in Fig. 1 (b). 8 1 (b) t Fig. 1. (a) A fully developed double-scroll-like chaotic attractors of the isolate delayed Hopﬁeld neural network (17). (b) Impulsive synchronization process of the state variables in the controlled coupled delayed neural network (16).
The Stability of Nonlinear Dissipative Systems. IEEE Transaction on Automatic Control 21 (3) (1996) 708-711 12. : Topics in Control Theory. Birkhauser, Boston, USA, 1993 13. : Fuzzy Synchronization of Chaos Using Gray Prediction for Secure Communication. IEEE International Conference on Systems, Man, Cybernetics 4 (2004) 3104-31099 14. : Adaptive Fuzzy Observer Based on Synchronization Design and Secure Communications of Chaotic Systems. Chaos, Soliton and Fractals 27 (4) (2006) 930-940 15. : Adaptive Fuzzy Synchronization of Discrete-Time Chaotic Systems.