Title :
Full Factorial Design Based ANFIS Model for Complex Systems
Author :
Buragohain, M. ; Mahanta, C.
Author_Institution :
Dept. of Electron. & Commun. Eng., Indian Inst. of Technol., Guwahati
Abstract :
In this paper we propose an adaptive network based fuzzy inference system (ANFIS) model where the number of data pairs employed for training is minimized by application of an engineering statistical technique called full factorial design. Our proposed method is experimentally validated by applying it to the benchmark Box and Jenkins gas furnace data. By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced as compared to the number of data required for the conventional ANFIS method. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It was found that our model compares favourably well with conventional ANFIS model
Keywords :
adaptive systems; fuzzy neural nets; inference mechanisms; learning (artificial intelligence); statistical analysis; ANFIS; adaptive network based fuzzy inference system model; complex system; engineering statistical technique; full factorial design; gas furnace data; Adaptive systems; Artificial neural networks; Data engineering; Design engineering; Furnaces; Fuzzy neural networks; Fuzzy systems; Large-scale systems; Neural networks; Uncertainty; ANFIS; Full Factorial Design (FFD); complex systems; subtractive clustering;
Conference_Titel :
India Conference, 2006 Annual IEEE
Conference_Location :
New Delhi
Print_ISBN :
1-4244-0369-3
Electronic_ISBN :
1-4244-0370-7
DOI :
10.1109/INDCON.2006.302803
