NDLI: A Model Checking Technique for the Verification of Fuzzy Control Systems

Content Provider	IEEE Xplore Digital Library
Author	Intrigila, B. Magazzeni, D. Tofani, A. Melatti, I. Tronci, E.
Copyright Year	2006
Description	Author affiliation: Dip. di Matematica Pura ed Applicata, Univ. di Roma "Tor Vergata" (Intrigila, B.)
Abstract	Fuzzy control is well known as a powerful technique for designing and realizing control systems. However, statistical evidence for their correct behavior may be not enough, even when it is based on a large number of samplings. In order to provide a more systematic verification process, the cell-to-cell mapping technology has been used in a number of cases as a verification tool for fuzzy control systems and, more recently, to assess their optimality and robustness. However, cell-to-cell mapping is typically limited in the number of cells it can explore. To overcome this limitation, in this paper we show how model checking techniques may be instead used to verify the correct behavior of a fuzzy control system. To this end, we use a modified version of the Murphi verifier, which ease the modeling phase by allowing to use finite precision real numbers and external C functions. In this way, also already designed simulators may be used for the verification phase. With respect to the cell mapping technique, our approach appears to be complementary; indeed, it explores a much larger number of states, at the cost of being less informative on the global dynamic of the system
Sponsorship	IEEE Comput Intelligence Soc. Eur. Soc. for Fuzzy Logic and Technol. Eur. Neural Networks Soc. Int. Assoc. for Fuzzy Set in Manage. and Economy Japan Soc. for Fuzzy Theor. and Soc. for Fuzzy Theor. and Intelligent Informatics Taiwan Fuzzy Syst. Assoc. World Wide Web Bus. Intelligence Hungarian Fuzzy Assoc. Univ. of Canberra
Starting Page	536
Ending Page	542
File Size	286118
Page Count	7
File Format	PDF
ISBN	0769525040
DOI	10.1109/CIMCA.2005.1631319
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-28
Publisher Place	Austria
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robust control Fuzzy control Costs Neural networks Control system synthesis Cities and towns Control systems Sampling methods Robustness State-space methods
Content Type	Text
Resource Type	Article

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