This paper analyzes the global optimization function of the particle swarm optimization (PSO), the algorithm can be improved bp neural network learning strategies, to make up for the lack of bp neural network parameter settings.

This paper analyzes the optimization features with a global search of particle cluster optimization algorithm (PSO), the algorithm is able to improve traditional BP neural network learning strategies, and make up BP neural network parameters are set.

This article analyzed had an overall situation optimization function grain of subgroup to optimize algorithm (PSO), this algorithm could improve traditional the BP neural network study strategy, made up the BP neural network parameter establishment the insufficiency.

This paper analyzes with Particle Swarm Optimization algorithm for global optimization feature (PSO), the algorithm of BP neural network to improve the traditional learning strategy, make up for the inadequacy of BP neural network parameter settings.

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