Incremental projection learning algorithm for constructing radial basisfunction network based on the adjustment of the sample operator
采样算子调整的径向基网络增量映射学习算法

Firstly,we adjusted the parameters of the radial basis function neural network, including the center and the covariance of the base function,and readoped the sampling operator of the algorithm;Then we raised a threshold to decide whether the radial basis function neural network needs a new neuron to reduce the output error of the system or not,and we could get it through calculating the correlation of the system's hidden neurons' base functions and the correlation between the (neuron's) base functions and stock functions.Through iteration of training,adjustment and selection,this method could adjust the system structure logically. Because of the simplicity of the new method,the improved incremental projection learning (IPL) algorithm's calculation speed was faster than before. The simulation results showed that the new algorithm can induce a simpler network structure than the former algorithm,and the output of the new IPL inducing network is more accurate than before.