Improvement of RBF Neural Network by AdaBoost Algorithm Combined with PSO

Abstract

The traditional RBF neural network has the problem of slow training speed and low efficiency, this paper puts forward the algorithm of improvement of RBF neural network by AdaBoost algorithm combined with PSO, to expand the application range of the RBF neural network. Firstly, it preprocesses the sample data in training set, and initialize the weights of test data; Secondly, it optimizes and chooses different implied layer functions and network learning parameters by using the improved PSO algorithm, to produce different types of RBF weak predictor, and repeatedly train the sample data by using Matlab tools; Finally, it constructs multiple generated RBF weak predictors to strong predictors by using AdaBoost iterative algorithm. It chooses data sets of UCI database to do the simulation experiment, and the simulation results show that the proposed algorithm further reduces the mean absolute error, compared with the traditional RBF neural network prediction, the experiment has improved the prediction precision of the network, to provide a reference for RBF neural network prediction.