关键词: 前向神经网络, BP算法, 填充函数, 全局优化, 局部极小点

Abstract: A novel global optimization training algorithm was constructed for the feed-forward neural networks to which the BP algorithm is easy to fall into the local minimum point for improper choice of initial weights. A new filled-function is therefore proposed with its nature of being filled-in proved. Then, the function is combined with BP algorithm to construct a global hybrid optimization algorithm. When it is used to train the neural networks in which the error function has fell into a local minimum point, it can apply the filled-function to helping the error function to uninterruptedly get rid of the point till a globally optimum point is found. So, its best advantage is independent of the chosen initial weights which makes the BP algorithm easy to fall into local minimum point. The simulation results and theoretic analysis show that this new algorithm is of efficiency and superiority.