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 offered have been conscientiously reviewed and chosen from a number of submissions. The papers are equipped in following issues: computational neuroscience, cognitive technological know-how, neural community versions, studying algorithms, balance and convergence research, kernel equipment, huge margin tools 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 information discovery and different purposes of neural networks.
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Extra info for Advances in Neural Networks – ISNN 2013: 10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part II
The introduction of chaotic theory into basic EDA is an important improvement. EDA can converge fast, but sometimes the fast convergence happens in the first few iterations and relapses into a local optimum easily . By introducing the chaos theory, we can avoid from the local optimum as well as to increase the speed of reaching the optimal solution. The detailed procedure of our proposed CEDA approach to the optimization of flight control parameters can be described as follows. Step1: Initialize the detailed parameters of the estimation of distribution algorithms (EDA) such as the population size, coding length and so on.
For initial condition xpd (0)(p ∈ K) which generated the recurrent reference orbit, and with corresponding to initial condition xp (0) in a close vicinity of ϕdζ , we have that (i) all signals in the closed-loop subsystem remain bounded and the state tracking error xp = xp (t) − xpd (t) exponentially converges to a small neighborhood around zero. (ii) the closed-loop systems (1) is locally asymptotically stable for any switching signal that has the average dwell time property with τ > logμ/λ. (iii) 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.
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.