Download Advances in Neural Networks – ISNN 2013: 10th International by Qinglai Wei, Derong Liu (auth.), Chengan Guo, Zeng-Guang PDF

By Qinglai Wei, Derong Liu (auth.), Chengan Guo, Zeng-Guang Hou, Zhigang Zeng (eds.)

The two-volume set LNCS 7951 and 7952 constitutes the refereed court cases of the tenth overseas Symposium on Neural Networks, ISNN 2013, held in Dalian, China, in July 2013. The 157 revised complete papers provided have been rigorously reviewed and chosen from various submissions. The papers are equipped in following subject matters: computational neuroscience, cognitive technology, neural community versions, studying algorithms, balance and convergence research, kernel tools, huge margin equipment and SVM, optimization algorithms, varational tools, keep an eye on, robotics, bioinformatics and biomedical engineering, brain-like platforms and brain-computer interfaces, information mining and data discovery and different functions of neural networks.

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Read or Download Advances in Neural Networks – ISNN 2013: 10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part II PDF

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Additional info for Advances in Neural Networks – ISNN 2013: 10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part II

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The variation of the chaotic variable has a delicate inherent rule in spite of the fact that its variation looks like in disorder. Therefore, after each search round, we can conduct the chaotic search in the neighborhood of the current optimal parameters by listing a certain number of new generated parameters through chaotic process. In this way, we can make use of the ergodicity and irregularity of the chaotic variable to help the algorithm to jump out of the local optimum as well as finding the optimal parameters.

Remark 3. Consider the closed-loop system consisting of the plant (1), the reference model(3), and the neural learning controller(4) with neural weights W being given as W = meant∈[ta ,tb ] W (t). For initial condition xpd (0)(p ∈ K) which generated the recurrent reference orbit ϕdζp (p ∈ K), here, different reference models are designed not by initial conditions but by system parameters, and with corresponding to initial condition xp (0) in a close vicinity of ϕdζp , we have that all signals in the closed-loop systems (1) remain bounded, and the state tracking error x = x(t) − xd (t) exponentially converges to a small neighborhood around zero when satisfying the ADT property.

Control 56(8), 1948–1952 (2011) 14. : Decentralized robust adaptive control for the multiagent system consensus problem using neural networks. IEEE Trans. , Man, Cybern. Part B: Cybern. 39(3), 636–647 (2009) 15. : Distributed adaptive control for synchcronization of unknown nonlinear networked systems. Automatica 26(12), 2014–2021 (2010) 16. : Cooperative adaptive control for synchronization of secondorder systems with unknown nonlinearities. Int. J. Robust Nonlin. 21(13), 1509–1524 (2011) 17. : Neural-network-based adaptive leader-following control for multiagent systems with uncertainties.

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