Title :
Nonlinear System Identification Based on Adaptive Neural Fuzzy Inference System
Author :
Zhi-xiang, Hou ; He-qing, Li
Author_Institution :
Coll. of Automobile & Mech. Eng., Changsha Univ. of Sci. & Technol.
Abstract :
System identification is the basis of designing control system, and it is very difficulty to identify the nonlinear system today. A new identification method of nonlinear system based adaptive neural fuzzy inference system (ANFIS) is provided in this paper, which assembles the advantages of fuzzy theory and neural networks. The structure and algorithms of ANFIS is introduced firstly, then a nonlinear function is tested using the method, and the simulation results show that ANFIS is very effective to identify the nonlinear system
Keywords :
adaptive systems; fuzzy neural nets; fuzzy set theory; inference mechanisms; nonlinear control systems; ANFIS; adaptive neural fuzzy inference system; control system design; fuzzy theory; neural networks; nonlinear system identification; Adaptive systems; Assembly systems; Control systems; Fuzzy neural networks; Fuzzy systems; Inference algorithms; Neural networks; Nonlinear control systems; Nonlinear systems; System identification;
Conference_Titel :
Communications, Circuits and Systems Proceedings, 2006 International Conference on
Conference_Location :
Guilin
Print_ISBN :
0-7803-9584-0
Electronic_ISBN :
0-7803-9585-9
DOI :
10.1109/ICCCAS.2006.285085
