The Research Of Pulse Signals Based On ACC-RBF Neural Network

Modern research on the pulse signals indicates that the function of heart, the complexion of vas and the quality of blood response to pulse-feeling. So pulse signal can show health condition impersonality.Radial basis function neural network (RBFNN) is a three-layer forward network。It have been extensively used in such diverse fields as pattern recognition, system design, function approach, signal processing, adaptive signal filter, nonlinear time series analysis and so on, owing to their features of simple architectures and brief training requirements.In this paper we introduce an improved method of Ant Colony Clustering algorithm,then use the method to analyze the signal of pulse-feeling. Considering the characteristic differences between the pulse signals of heroin addicts and healthy persons, we successfully use RBF network based on K-means clustering and RBF network based on Ant Colony Clustering algorithm to identify heroin addicts from the pulse signals of 22 heroin addicts and 22 healthy persons.The research shows that the network using ACC algorithm to identify the number of hidden nodes and PSO algorithm to train the weights of hidden layer has better performance of approximation and generalization than the network using K-means clustering algorithm with LMS algorithm.