Takagi-Sugeno recurrent fuzzy neural networks for identification and control of dynamic systems

Abstract

In this paper, we propose a Takagi-Sugeno recurrent fuzzy neural network (TSRFNN) for the identification and control of nonlinear dynamic systems. The TSRFNN combines the recurrent multi-layered connectionist network with the dynamic Takagi-Sugeno (TS) fuzzy model. The temporal information is embedded in the recurrent structure by adding feedback connections between the state layer and the input layer of the fuzzy neural net (FNN). Based on the derived dynamic backpropagation (DBP) and recursive least squares (RLS) algorithms, the parameters in the TSRFNN are adjusted online. Compared with the traditional recurrent FNNs (RFNNs), the proposed TSRFNN not only has a smaller network structure and a smaller number of network parameters, but also a faster convergence speed and better learning performance.