GA-RBFNN learning algorithm for complex classifications
Fuzan Chen · Journal of systems engineering · 2006
This paper proposes an adaptive learning algorithm,GA_RBFNN,to build a RBF neural network(RBFNN) model.The algorithm utilizes GA's parallel_search ability to improve the classification accuracy of the RBFNN.Firstly,the initial network hidden structure of a RBFNN model is determined by the traditional decayed_radius clustering algorithm.Then the hidden centers of a RBFNN are modified by a specially designed GA,which is based on the matrix-form mixed encoding scheme with a control vector for regulating the structure of a RBFNN,and the new genetic operators are presented correspondingly.The pseudo-inverse algorithm is adopted to train the weights between the hidden layer and the output layer.Finally,experiments are implemented on datasets as Iris,WINES,and Glass,which shows that the proposed algorithm has higher classification ability compared with the conventional methods.