Linearization learning method of BP neural networks

Feedforward multi-layer neural networks have very strong mapping capability that is based on the non-linearity of the activation function, however, the non-linearity of the activation function can cause the multiple local minima on the learning error-surfaces, which affect the learning rate and solving optimal weights. This paper proposes a learning method linearizing non-linearity of the activation function and discusses its merits and demerits theoretically. Supported by Wuhan Shenguang Project, the National Natural Science Foundation of China and the 863 High Techology Project Zhou Shaoqian: born in Mar. 1962,Ph. D graduate student