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Traditional gradient-based training algorithms have been known to suffer from local minima and have heavy computation load for obtaining the derivative information. The particle swarm optimization (PSO) method was used as a training algorithm of neural networks to improve the convergence rate. However, as the network architecture grows, the size of swarm increases exponentially, which increase the...
Support vector machine (SVM) has become a popular classification tool, but training SVM consumes large memory and computation time. Traditional methods can not overcome above shortcomings. This paper presents a novel SVM training method based improved chaotic particle swarm optimization (CPSO) algorithm. Firstly, a new chaotic search model using improved circle map is introduced. Then this new model...
This paper proposes a multi-agent decision model based on recurrent neural networks and particle swarm optimization technology. In this paper, the recurrent neural network is used for strategy decision controller. The inputs of the recurrent neural network are decided by the last strategies of other agents. Then the outputs determine the next strategy that the agent will choose. The weight values...
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