A Biological Mechanism Based Structure Self-Adaptive Algorithm for Feedforward Neural Network and Its Engineering Applications

Abstract

Feedforward neural network (FNN) is an information processing system that simulates human brain function to a certain extent by referring to the structure of a biological neural network and the working mechanism of biological neurons. As the most important part, the architecture of FNN essentially influences its application performance. This paper proposed a structure self-adaptive algorithm for FNN (SSAFNN) based on biological mechanism. First, self-adaptive neuron growing and pruning indexes are proposed based on the idea of biological neuron grow factor and neuron competition, respectively. The FNN structure is dynamically adjusted according to the growing and pruning indexes of hidden neurons. Second, the connect weights of FNN are automatically adjusted during the self-organizing process and trained by gradient descent method during the learning process. Third, the theoretical analysis is given that this proposed algorithm not only optimized the network structure but also ensured the convergence and performance of FNN. The proposed SSAFNN is tested on the benchmark problems in the field of classification and prediction and is applied in the engineering problems of anchor bolt non-destructive testing and wastewater effluent ammonia nitrogen predicting. The experiment results reveal the good performance and potentiality of SSAFNN in industrial applications.