Titlebook: Advances in Swarm Intelligence; First International Ying Tan,Yuhui Shi,Kay Chen Tan Conference proceedings 2010 Springer-Verlag Berlin Hei

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Radial Basis Function Neural Network Based on PSO with Mutation Operation to Solve Function Approximthm. This algorithm combines Particle Swarm Optimization algorithm (PSO) with mutation operation to train RBFNN. PSO with mutation operation and genetic algorithm are respectively used to train weights and spreads of oRBFNN, which is traditional RBFNN with gradient learning in this article. Sum Squa

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A System Identification Using DRNN Based on Swarm Intelligenceation during the past decade. In this paper, a learning algorithm for Original Elman neural networks (ENN) based on modified particle swarm optimization (MPSO), which is a swarm intelligent algorithm (SIA), is presented. MPSO and Elman are hybridized to form MPSO-ENN hybrid algorithm as a system ide

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Force Identification by Using SVM and CPSO Techniqued utilizes a new SVM-CPSO model that hybridized the chaos particle swarm optimization (CPSO) technique and support vector machines (SVM) to tackle the problem of force identification. Both numerical simulations and experimental study are performed to demonstrate the effectiveness, robustness and app

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0302-9743 onstitute the proceedings of the International Conference on Swarm Intelligence (ICSI 2010) held in Beijing, the capital of China, during June 12-15, 2010. ICSI 2010 was the ?rst gathering in the world for researchers working on all aspects of swarm intelligence, and providedan academic forum for th

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David Beech (Chairman of IFIP WG 2.7)lect parameters of SVR. The proposed approach is used for forecasting logistics demand of Shanghai, The experimental results show that the above method obtained lesser training relative error and testing relative error.