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 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 rigorously reviewed and chosen from various submissions. The papers are equipped in following themes: computational neuroscience, cognitive technology, neural community types, studying algorithms, balance and convergence research, kernel tools, huge margin equipment and SVM, optimization algorithms, varational tools, regulate, robotics, bioinformatics and biomedical engineering, brain-like structures and brain-computer interfaces, information mining and data discovery and different functions of neural networks.

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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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It is proved that with the developed controllers, the state of each agent synchronizes to that of the leader for any undirected connected graphs even when only a fraction of the agents in the network have access to the state information of the leader, and the distributed tracking errors are uniformly ultimately bounded. A sufficient condition to the existence of the distributed controllers is that each agent is stabilizable and detectable. Future works include an extension to the directed network topologies.

Springer, Heidelberg (2004) 4. : AR-Drone as a Platform for Robotic Research and Education. , Gottscheber, A. ) EUROBOT 2011. CCIS, vol. 161, pp. 172–186. Springer, Heidelberg (2011) 5. html 6. : Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Boston (2002) 7. : A Survey of Optimization by Building and Using Probabilistic Models. Computational Optimization and Applications 21, 5–20 (2002) 8. : From recombination of genes to the estimation of distributions I.

D) σ = 10−1 . Optimal Tracking Control Scheme T 9 T where x(k) = x1 (k) x2 (k) , u(k) = u1 (k) u2 (k) . 1 −x1 (k)x2 (k) f (x(k)) = , g(x(k)) = . 5 cos(k) ] . The performance index function is defined as (3), where Q = R = I. We use neural networks to implement the iterative ADP algorithm. The critic network and the action network are chosen as three-layer BP neural networks with the structures of 2–8–1 and 2–8–2, respectively. We choose four approximation errors σ = 10−6 , σ = 10−4 , σ = 10−3 , σ = 10−1 , the iterative performance index functions are shown in Fig.

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