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

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 lawsuits 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 various submissions. The papers are geared up in following issues: computational neuroscience, cognitive technological know-how, neural community types, studying algorithms, balance and convergence research, kernel tools, huge margin tools and SVM, optimization algorithms, varational tools, keep watch over, robotics, bioinformatics and biomedical engineering, brain-like platforms and brain-computer interfaces, information mining and information discovery and different purposes of neural networks.

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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. Binary parameters. -P. ) PPSN 1996. LNCS, vol. 1141, pp. 178–187. Springer, Heidelberg (1996) 9. : Chaotic Artificial Bee Colony Approach to Uninhabited Combat Air Vehicle (UCAV) Path Planning.

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. The experimental results in section 4 show the efficiency of our algorithm. 2 CEDA Approach for Flight Control Parameters Optimization Chaotic estimation of distribution algorithm (CEDA) is a combination of chaos theory and basic EDA.

Then the dynamics of δ˜ can be written as ˜ δ˜˙ =(IN ⊗ A + c1 H ⊗ BK)δ˜ + c2 (IN ⊗ B)sgn((H ⊗ K)δ) + (IN ⊗ F C)˜ x − (1 ⊗ B)r. (16) Finally, introduce a parameter dependent Riccati equation ATe Pe + Pe Ae + Qe + κM M T = 0 M = C T − Pe B, (17) where Qe , Pe ∈ Rn×n are positive definite. 0 < κ ≤ κmax define the largest set within which there exists a positive definite solution for Pe . 1. Consider the multi-agent systems (1) with the leader node (2) under Assumption 1. Suppose the network G is undirected and connected, and at least one agent has access to the leader node.

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