DocumentCode :
3364952
Title :
A New ANFIS Based Learning Algorithm for CMOS Neuro-Fuzzy Controllers
Author :
Peymanfar, A. ; Khoei, A. ; Hadidi, Kh
Author_Institution :
Urmia Univ., Urmia
fYear :
2007
fDate :
11-14 Dec. 2007
Firstpage :
890
Lastpage :
893
Abstract :
This paper presents a new learning procedure for ANFIS (Adaptive-Network-based Fuzzy Inference System), a fuzzy inference system implemented in the framework of adaptive networks. By using this new algorithm, the ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. This algorithm is a combination of perceptron neural network and hybrid learning algorithm, but this is convenient than hybrid learning procedure. The main purpose of this method is to provide a powerful algorithm to program CMOS fuzzy controllers, considering CMOS implementation limits. Simulation results are provided to demonstrate the capability of proposed algorithm.
Keywords :
CMOS integrated circuits; fuzzy control; neurocontrollers; ANFIS; CMOS neuro-fuzzy controllers; adaptive-network-based fuzzy inference system; hybrid learning algorithm; Adaptive systems; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Humans; Inference algorithms; Laboratories; Learning systems; Microelectronics; Neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronics, Circuits and Systems, 2007. ICECS 2007. 14th IEEE International Conference on
Conference_Location :
Marrakech
Print_ISBN :
978-1-4244-1377-5
Electronic_ISBN :
978-1-4244-1378-2
Type :
conf
DOI :
10.1109/ICECS.2007.4511134
Filename :
4511134
Link To Document :
بازگشت