A New Global Optimization Algorithm for Training Feedforward Neural Networks and Its Application
训练前向神经网络的全局优化新算法及其应用

This paper proposes a new global optimization technique in which combines the filled function method and BP algorithm for Training feedforward neural networks. In this algorithm, the BP algorithm finds one of local minimal points first, the filled function method finds the point that is lower than the minimal point previously found. By repeating these processes, a global minimal point can be obtained at last. Practical examples indicate that the method works well in avoiding sticking in local minima. Compared with usual BP training algorithm, this new global optimization algorithm is more efficient and has a higher accuracy in application to establishing production quality model.