This study presents a functional-link-based fuzzy neural network (FLFNN) structure for temperature control. The proposed FLFNN controller uses functional link neural networks (FLNN) that can generate a nonlinear combination of the input variables as the consequent part of the fuzzy rules. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the corresponding weights of the FLNN. Simulation result of temperature control has been given to illustrate the performance and effectiveness of the proposed model.
|Name||2007 IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTATIONAL INTELLIGENCE, VOLS 1 AND 2|
|Conference||2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007|
|Period||1/04/07 → 5/04/07|