A Self-Learning Fuzzy Control Method Based on RBF Neural Networks
This paper proposes an self-learning fuzzy control method based on an improved radial basis function neural networks (RBFNN). The architecture of the proposed approach is comprised of a fuzzy controller and an RBFNN. For such an architecture, firstly, an analytical formula is employed to design fuzzy controller. Then, RBFNN based on an efficient locally regularized forward recursive (LRFR) algorithm is described and employed to learn the model of the plant. Finally, the parameters of fuzzy controller are tuned online by self-learning algorithm based on RBFNN. The simulation studies for a heating, ventilation and air-conditioning (HVAC) system demonstrates the validity and performance of the proposed learning algorithm.
fuzzy control heating ventilation and airconditioning (HVAC) system radial basis function (RBF) neural networks regularization parameter
Dajun Du Xue Li
Department of Automation School of Mechanical Engineering and Automation, Shanghai University Shanghai, China
国际会议
太原
英文
370-374
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）