关键词: 单极Sigmoid函数, 神经网络分类器, 权值直接确定法, 数值实验

Abstract: A neural network classifier with the hidden neurons activated by unipolar Sigmoid function was constructed and investigated in this paper. The thresholds of hidden neurons and weights between the input layer and the hidden layer of the neural network were randomly generated. The psedoinverse-type Weights Direct Determination (WDD) method was applied to determining the weights between the hidden layer and the output layer. Moreover, a Structure Automatic Determination (SAD) algorithm with pruning-while-growing and twice-pruning policies was proposed to determine the optimal structure of the neural network. The numerical experimental results demonstrate that the SAD algorithm can determine the optimal structure of the neural network quickly and effectively and the neural network classifier has a satisfactory performance.

Key words: unipolar Sigmoid function, neural network classifier, Weights Direct Determination (WDD) method, numerical experiment