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）

摘要： 提出采用GA-BP贝叶斯算法来建立优化设计近似模型。该算法是一种新型神经网络训练算法，它以提高网络的泛化性能为主旨，其训练目标是获取对应于后验分布最大值的权值向量。以方形扁平封装器件为例，采用GA-BP贝叶斯算法建立了温度场分析的近似模型，基于它对封装散热结构进行了优化，并与L-M BP算法进行了对比。结果表明，基于GA-BP贝叶斯算法的温度场分析近似模型，对芯片中心温度的预测精度更为理想，并且受随机因素的影响很小。GA-BP贝叶斯算法克服了现有网络训练算法对初始权值敏感、建模精度不高的缺点，在工程优化设计中具有实用价值。

关键词: 优化设计, 热设计, 神经网络