NDLI: ANFIS Modeling of Nonlinear System Based on Combined FFD-Vfold Technique

Content Provider	IEEE Xplore Digital Library
Author	Buragohain, M. Mahanta, C.
Copyright Year	2006
Description	Author affiliation: Indian Inst. of Technol. Guwahati, Guwahati (Buragohain, M.; Mahanta, C.)
Abstract	Adaptive network based fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modeling and control of ill-defined and uncertain systems. ANFIS is based on the input-output data pairs of the system under consideration. The size of the input-output data set is very crucial when the data available is very less and the generation of data is a costly affair. Under such circumstances, optimization in the number of data used for learning is of prime concern. In this paper we have proposed an ANFIS based system modeling where the number of data pairs employed for training is minimized by application of a combine technique of full factorial design (FFD) and V-fold technique. The full factorial design technique is used to fine tune the data optimization process which is obtained by the V-fold technique. Our proposed method is experimentally validated by applying it to two separate sets of data obtained from the benchmark Box and Jenkins gas furnace data set and the thermal power plant of the NEEPCO (North Eastern Electrical Power Corporation Limited) . By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced to around one-eighth of the requirement for the conventional ANFIS method. This result in the saving of the computation time as well as computation complexity is remarkably reduced. 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 favorably well with conventional ANFIS model.
Starting Page	2462
Ending Page	2467
File Size	4161266
Page Count	6
File Format	PDF
ISBN	1424407257
DOI	10.1109/ICIT.2006.372647
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-15
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nonlinear systems Adaptive systems Programmable control Adaptive control Fuzzy control Fuzzy neural networks Fuzzy systems Intelligent networks Uncertain systems Modeling
Content Type	Text
Resource Type	Article

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