Complexity Research on Second Class Orthogonal Weight Function Neural Networks

Abstract

Weight function neural network is a new kind of neural network developed in recent years, which has many advantages, such as finding globe minima directly, good performance of generalization, extracting some useful information inherent in the problems and so on. Time complexity is an important measure of algorithm. This paper studies the complexity of neural network using second class orthogonal weight functions. The results indicate that the neural network has a linear relationship with the dimensions of input layer and output layer, an O (n3) relationship with the number of samples. Finally gives some simulation experiments for time complexity.