Using context‐sensitive neuro‐fuzzy systems for nonlinear system identification
Cheng‐Jian Lin, Chi-Nan Tsai · Journal of the Chinese Institute of Engineers · 2004
This paper describes a self‐constructing neuro‐fuzzy system for the identification of nonlinear dynamic systems with unknown parameters. The proposed model takes the form of a context‐sensitive module in which a fuzzy system is used as a function module and a multilayer neural network is used as a context module. This context module can effectively reduce the output rms error. The proposed neuro‐fuzzy system with a decomposed structure reduces complexity and thus accelerates the learning process. An on‐line self‐constructing learning algorithm is proposed. The structure learning is based on the fuzzy similarity measure and the parameter learning is based on the supervised gradient descent method. Simulations demonstrate that the proposed model is quite effective.