Abstract
In this paper, we investigate the optimization of BP neural network. An optimized BP neural network model, adaptive BP neural network based on PSO and PCA algorithms (TPPMA) is proposed to improve the training speed and increase prediction accuracy. By introducing Momentum backpropagation and adaptive learning rate into BP Neural Network, one can reduce the possibility of local optimization. For the architecture of BP Neural Network, the initial connection weight is determined by Particle Swarm Optimization (PSO) and the number of hidden nodes is decided by three-division method. In the model, Principal Component Analysis (PCA) is used to reduce the dimension of the sample. Simulation result demonstrates that TPPMA method is efficient and can get promising results with less time compared with other results.
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