Design of a self-organizing reciprocal modular neural network for nonlinear system modeling

Aiming to improve the model's generalization performance for nonlinear system modeling, a self organizing reciprocal modular neural network (SORMNN) is proposed in the present study, which imitates the modular structure with inter-module connections observed in human brains. The inter module connections in SORMNN are built by inputting the output of each subnetwork to other subnetworks. All subnetworks work in parallel to process the allocated features, and the structure of each subnetwork is designed to be self-organized by using a growing and pruning algorithm based on the contribution of hidden neurons. An improved Levenberg-Marquardt (LM) algorithm using a sliding window is conducted to update the parameters of SORMNN, which makes SORMNN available for solving online problems. To validate the effectiveness of the proposed model, SORMNN is tested on chaotic benchmark time series prediction, four UCI benchmark problems and a practical problem for biochemical oxygen demand prediction in wastewater treatment process. Experimental results demonstrate that SORMNN exhibits both a higher training accuracy and a better generalization ability for nonlinear system modeling than other modular neural networks, and the inter-module connections have a positive effect on the superior performance of the proposed model and can make the network structure compact. (c) 2020 Elsevier B.V. All rights reserved.