Using self-constructing recurrent fuzzy neural networks for identification of nonlinear dynamic systems
by Qinghai Li; Ye Lin; Rui-Chang Lin; Hao-Fei Meng
International Journal of Modelling, Identification and Control (IJMIC), Vol. 33, No. 4, 2019

Abstract: In this paper, the self-constructing recurrent fuzzy neural network (SCRFNN) is applied for nonlinear dynamical system identification (NDSI). The SCRFNN is a novel fuzzy neural network (FNN) by adding a recurrent path in each node of the hidden layer of self-constructing FNN, which contains two learning phases. Specifically, the structure learning is based on partition of the input space and the parameter learning is based on the supervised gradient descent method using a delta adaptation law. The SCRFNN can decrease the minimum firing strength in each learning cycle and the number of hidden neurons which is an FNN with high accuracy and compact structure compared with several other neural networks. The performance of SCRFNN in NDSI is further verified in simulation.

Online publication date: Fri, 29-May-2020

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