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.
Author: Yuanyuan Wang, Xiang
Li
Jounal Code: jptkomputergg160073
