A modification of Sugeno-Yasukawa Modeler to improve Structure Identification Phase
Amir Hossein Hadad, Saeed Shiry Ghidary, Saeed Bahrami, Saeed Shahbazi
Iran Telecommunication Research Center & Computer Engineering and Information Technology Faculty
(ITRC), Amirkabir University of Technology, Abhar Azad University, ITRC
http://www.itrc.ac.ir, http://ce.aut.ac.ir, http://ce.sharif.edu

Abstract:

Structure identification is one of the most significant steps in Fuzzy modeling of a complex system. Efficient structure identification requires good approximation of the effective input data. Misclassification of effective input data can highly degrade the efficiency of the inference of the fuzzy model. In this paper we present a modification to Sugeno-Yasukawa modeler to improve structure identification by increasing the accuracy of effective input data detection. There exist some middle models in the Sugeno-Yasukawa modeling process which a combination of them will result in the final fuzzy model of the system. In the original modeling process parameter identification is only done for the final fuzzy model. By doing the parameter identification for the middle fuzzy models, we have highly improved the accuracy of theses middle models. The RC (Regularly Criterion) error has been reduced 53% for middle fuzzy models and 67% in the final model for the sample function in formula (3). This accuracy increase, result in a better detection of effective parameters among input data records of a system. We have also used our new modeling method for a sample application and by modeling the system we have reduced input data needed for reasoning from 17 to 6. This caused a 60 % boost in the reasoning process of input data.

Keywords: Fuzzy Logic, Fuzzy Modeling, Structure Identification, Parameter Identification, Black-box systems.

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Biographies:

	Amir Hossein Hadad, was born in Shiraz, Iran, in 1979. He received his B.S. degree in Computer Software Engineering from the Amirkabir University of Technology in 2002. From 2002 to 2005 He had studied in M.Sc of Artificial Intelligence in Amirkabir University of Technology. His research interests include Fuzzy Modeling, Information Retrieval, Mobile Agents and Active networks.
	Saeed Shiry Ghidary, was born in Zanjan, Iran. He received his B.S. degree in Electronic engineering from the Amirkabir University of Technology in 1990. From 1991 to 1994 he had studied in M.Sc of Computer architecture in Amirkabir University of Technology. He is an advisor in Amirkabir University of Technology as Computer Engineering Faculty since 2003. His research interests include fuzzy modeling, Machine Learning Robotics and Fuzzy Modeling.
	Saeed Bahrami, was born in Qazvin, Iran, in 1971. He received his B.S. degree in Computer Software Engineering from the Qazvin Azad University in 1997. From 1999 to 2001 He had studied in M.Sc of Computer Software Engineering in Najafabad Azad University. At the present time he teaches in Qazvin and Abhar Branch Azad University. His research interests includes: Mobile agents, E-learning and Fuzzy Logic.
	Saeed Shahbazi, was born in Tehran, Iran, in 1980. He received his B.S. degree in Computer Software Engineering from the Iran University of Science and Technology in 2002. From 2002 to 2005 He had studied in M.Sc of Artificial Intelligence in Sharif University of Technology. His research interests include Fuzzy Systems, Ad hoc Networks, Sensor Networks, Distributed Artificial Intelligence, and Machine Learning.

BibTex:

(Full Paper , 751 KB)