A Self-Organizing Modular Neural Network for Nonlinear System Modeling

In this paper, a self-organizing modular neural network (SO-MNN) is proposed for nonlinear system modeling, in which the modular structure and sub-networks are optimized to improve the modeling accuracy and efficiency. First, the modular structure is constructed by seeking the maximum modularity degree of the whole neural network and the optimal hub center in each sub-network. Simultaneously, the task is divided into several sub-tasks. Then, according to the assigned sub-tasks, sub-networks are constructed based on an incremental radial basis function (RBF) neural network, whose performance can be guaranteed by a structure growing mechanism and an adaptive second-order learning algorithm. Finally, during the testing or application processes, a winner-take-all strategy is used to integrated all the sub-networks. The effectiveness of the proposed methodology is verified by two benchmark problems and a real-world application.