A self-constructing compensatory neural fuzzy system and its applications

A self-constructing compensatory neural fuzzy system (SCCNFS) for nonlinear system identification and control is proposed in this paper. The compensatory fuzzy reasoning method uses adaptive fuzzy operations of a neural fuzzy network to make the fuzzy logic system more adaptive and effective. An online learning algorithm is proposed to automatically construct the SCCNFS. The fuzzy rules are created and adapted as online learning proceeds through simultaneous structure and parameter learning. The structure learning is based on the fuzzy similarity measure and the parameter learning is based on the backpropagation algorithm. The advantages of the proposed learning algorithm are that it converges quickly and that the fuzzy rules that are obtained are more precise. The performance of SCCNFS compares excellently with other various existing models.