Structure Optimization for Feed-forward Neural Networks Based on Evolutionary Programming and Sequential Quadratic Programming
利用进化规划和逐步二次规划实现前馈神经网络的结构优化

In this paper, when evolutionary programming and sequential quadratic programming are applied to the structure optimization of feed-forward neural networks, a learning algorithm is proposed. The new algorithm retains the ability of stochastic global searching. It has better global convergence and very strong self-adaptive ability with environment. The efficiency of research work mentioned above has been shown by simulation and applications.