A self-organizing cascade neural network with random weights for nonlinear system modeling

Abstract

In this paper, a self-organizing cascade neural network (SCNN) with random weights is proposed for nonlinear system modeling. This SCNN is constructed via simultaneous structure and parameter learning processes. In structure learning, the units, which lead to the maximal error reduction of the network, are selected from the candidates and added to the existing network one by one. A stopping criterion based on the training and validation errors is introduced to select the optimal network size to match with a given application. In parameter learning, the weights connected with the output units are incrementally updated without gradients or generalized inverses, while the other weights are randomly assigned and no need to be tuned. Then, the convergence of SCNN is analyzed. Finally, the proposed SCNN is tested on two benchmark nonlinear systems and an actual municipal sewage treatment system. The experiment results show that the proposed SCNN has better performance on nonlinear system modeling than other similar methods.

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