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Title: Controller design for synchronization of an array of delayed neural networks using a controllable
Authors: Tang, Y
Wang, Z
Fang, J
Keywords: Swarm intelligence;Neural networks;Bernoulli stochastic variable;Probabilistic particle swarm optimization (CPPSO);Discrete and distributed delay
Issue Date: 2011
Publisher: Elsevier
Citation: Information Sciences, 181(20), 4715 - 4732, Oct 2011
Abstract: In this paper, a controllable probabilistic particle swarm optimization (CPPSO) algorithm is introduced based on Bernoulli stochastic variables and a competitive penalized method. The CPPSO algorithm is proposed to solve optimization problems and is then applied to design the memoryless feedback controller, which is used in the synchronization of an array of delayed neural networks (DNNs). The learning strategies occur in a random way governed by Bernoulli stochastic variables. The expectations of Bernoulli stochastic variables are automatically updated by the search environment. The proposed method not only keeps the diversity of the swarm, but also maintains the rapid convergence of the CPPSO algorithm according to the competitive penalized mechanism. In addition, the convergence rate is improved because the inertia weight of each particle is automatically computed according to the feedback of fitness value. The efficiency of the proposed CPPSO algorithm is demonstrated by comparing it with some well-known PSO algorithms on benchmark test functions with and without rotations. In the end, the proposed CPPSO algorithm is used to design the controller for the synchronization of an array of continuous-time delayed neural networks.
Description: This is the post-print version of the Article - Copyright @ 2011 Elsevier
ISSN: 0020-0255
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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