A Study on BP Networks Based on Weight-function Neuron

A kind of neuron model is proposed. In the model,the activation function with adjustable parameters is moved forward to the weight,that is the weight becomes a function with adjustable parameters,and the sum of these weight functions as the neuron output. Such neuron is called weight-function neuron. According to BP algorithm,the learning algorithm of feed-forward neural networks with the weight-function neuron is given. Simulation comparison results show that,in a given accuracy requirement,the BP neural network based on the weight-function neurons can converge in each training,and its average iterations is fewer,and its convergence speed is superior to traditional BP network,so it has good research and application value.