关键词: 优化设计, 热设计, 神经网络

Abstract: The GA-BP Bayesian algorithm is used to establish the approximation model for optimization design.This algorithm is a new NN training algorithm developed by the authors.Its aim is to improve the generalization ability of neural networks，and it trains a network with the purpose of obtaining the weights corresponding with the maximum posterior probability.Taking a quad flat package for example，the GA-BP Bayesian algorithm was used to establish the temperature-field analysis approximation model.Then the optimization design of the heat-dissipating structure was carried out based on it，and the comparison with the L-M backpropagation was made.The results show that the temperature-field analysis approximation models based on GA-BP Bayesian algorithm have higher prediction accuracy for chip center temperature，and that the prediction accuracy they have can hardly be effected by random factors.The GA-BP Bayesian algorithm overcomes the shortcomings of the current algorithms，such as the high sensitiveness to initial weights and the unsatisfactory modeling accuracy，and it is valuable in engineering optimization design.

Key words: optimization design, thermal design, Neural Network（NN）