Self-organizing fuzzy neural network with adaptive evolution strategy for nonlinear and nonstationary processes

Abstract

Fuzzy neural networks, which combine the strengths of fuzzy logic systems and artificial neural networks, prove to be effective in modeling industrial processes. However, because of the nonlinearity and nonstationarity exhibited in complex industrial processes, constructing an accurate model and maintaining its performance in uncertain environments have remained challenging. Hence, a self-organizing fuzzy neural network with an adaptive evolution strategy (AE-SOFNN) is proposed for nonlinear and nonstationary process modeling. First, a self-organizing mechanism based on the network learning accuracy and the activity of rules is developed to achieve a compact structure. Meanwhile, by integrating the least squares method and an improved second-order algorithm, a hybrid learning algorithm is applied to adjust network parameters. Then, an adaptive evolution strategy is proposed to enable the AE-SOFNN to better adapt to changes, aiming to ensure the accuracy and robustness of the constructed network in nonstationary environments. Specifically, an adaptive activation threshold based on generalization ability is developed to determine how to update, namely by either local updating or global updating. The variation of linear parameters during local updating is taken as an indicator of concept drift, helping to improve the global updating performance via the selection of appropriate samples. Finally, the effectiveness of the AE-SOFNN is evaluated by a chaotic time-series prediction problem and an industrial application, demonstrating the superiority of AE-SOFNN in modeling nonlinear and nonstationary processes.