Design of a self-organizing recurrent RBF neural network based on spiking mechanism

Based on the systemic investigation of recurrent neural network, a self-organizing recurrent radial basis function (SR-RBF) neural network which based on the spiking mechanism and improved Levenberg-Marquardt (LM) algorithm is proposed in this paper. The hidden neuron in the recurrent radial basis function (RRBF) can be added or pruned by computing the spiking strength of the connections between hidden and output neurons of RRBF neural network. Meanwhile, to ensure the accuracy of SR-RBF neural network, the parameters are trained by improved LM algorithm. The SR-RBF neural network is used for approximating the time-series prediction and classical non-linear functions. Finally, comparisons with other methods demonstrate that the SR-RBF neural network is more effective in terms of accuracy, generalization, and network structure.