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 foreign Symposium on Neural Networks, ISNN 2013, held in Dalian, China, in July 2013. The 157 revised complete papers awarded have been rigorously reviewed and chosen from a number of submissions. The papers are equipped in following themes: computational neuroscience, cognitive technology, neural community types, studying algorithms, balance and convergence research, kernel equipment, huge margin tools and SVM, optimization algorithms, varational equipment, regulate, robotics, bioinformatics and biomedical engineering, brain-like platforms and brain-computer interfaces, info mining and information discovery and different purposes of neural networks.

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

The two-volume set LNCS 7951 and 7952 constitutes the refereed complaints of the tenth foreign Symposium on Neural Networks, ISNN 2013, held in Dalian, China, in July 2013. The 157 revised complete papers provided have been conscientiously reviewed and chosen from a number of submissions. The papers are prepared in following themes: computational neuroscience, cognitive technological know-how, neural community versions, studying algorithms, balance and convergence research, kernel tools, huge margin tools and SVM, optimization algorithms, varational tools, regulate, robotics, bioinformatics and biomedical engineering, brain-like structures and brain-computer interfaces, facts mining and data discovery and different functions of neural networks.

Additional resources 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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Is (xi )]T and satisfies ϕi ≤ ϕiM with ϕiM ∈ R a positive constant; εi is the approximation error satisfying εi ≤ εiM with εiM ∈ R a positive constant; D is a sufficiently large domain D ⊂ Rn . 1 Distributed Output Feedback Tracking Control with Static Coupling Controller Design To begin with, the following distributed controller is proposed ui = uin − uiad . (4) The first term uin is a nominal controller designed as uin =c1 K eˆi + c2 sgn(K eˆi ), (5) where c1 ∈ R, c2 ∈ R are positive coupling gains; K ∈ Rm×n is a feedback matrix with K = −B T P, (6) Distributed Output Feedback Tracking Control of Uncertain Nonlinear MAS 15 where P is the unique positive definite solution to the following Riccati equation AT P + P A + Q − P BB T P = 0, (7) where Q ∈ Rn×n is positive definite; eˆi is defined as N aij (ˆ xi − x ˆj ), eˆi = (8) j=0 where x ˆi is an estimate of xi obtained using a state observer described by xi + Buin + F (yi − yˆi ), x ˆ˙ i = Aˆ yˆi = C x ˆi , (9) where F ∈ Rn×p is an observer gain matrix designed such that Ae = A − F C is Hurwitz.

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.

N } with λi being the ith diagonal element. An identity matrix of dimension N is denoted by IN . The Kronecker product is denoted by ⊗ with the properties (A ⊗ B)T = AT ⊗ B T , α(A ⊗ B) = (αA) ⊗ B = A ⊗ (αB), (A ⊗ B)(C ⊗ D) = (AC) ⊗ (BD), where A, B, C, D are matrices and α is a scalar. Graph Theory. Consider a network of multi-agent systems consisting of N agents and one leader. , nN } is a node set and E = {(ni , nj ) ∈ V × V} is an edge set with the element (ni , nj ) that describes the communication from node i to node j.

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