A Novel Recurrent Generalized Congruence Neural Network for Dynamical System Identification
Tianyun Yan, Hefei Ling, Shurong Zou · 2007
A novel recurrent generalized congruence neural network (RGCNN) is presented. Compared with traditional recurrent neural networks (RNNs), RGCNN has the following advantages: simple structure (4 layers), no time-consuming iterative derivative operations in updating weights, and fast convergence induced by modulo arithmetic of the generalized congruence neuron. Computer simulations on benchmark examples of dynamical system identification have successfully validated the performance of the proposed RGCNN.