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Approximation Of Multi-Valued Inverse Functions Using Clustering And Sugeno Fuzzy InferenceFinding the inverse of a continuous function can be challenging and computationally expensive when the inverse function is multi-valued. Difficulties may be compounded when the function itself is difficult to evaluate. We show that we can use fuzzy-logic approximators such as Sugeno inference systems to compute the inverse on-line. To do so, a fuzzy clustering algorithm can be used in conjunction with a discriminating function to split the function data into branches for the different values of the forward function. These data sets are then fed into a recursive least-squares learning algorithm that finds the proper coefficients of the Sugeno approximators; each Sugeno approximator finds one value of the inverse function. Discussions about the accuracy of the approximation will be included.
Document ID
20000032346
Document Type
Conference Paper
Authors
Walden, Maria A. (Agricultural and Technical Coll. of North Carolina Greensboro, NC United States)
Bikdash, Marwan (Agricultural and Technical Coll. of North Carolina Greensboro, NC United States)
Homaifar, Abdollah (Agricultural and Technical Coll. of North Carolina Greensboro, NC United States)
Date Acquired
August 19, 2013
Publication Date
February 22, 1998
Publication Information
Publication: NASA University Research Centers Technical Advances in Aeronautics, Space Sciences and Technology, Earth Systems Sciences, Global Hydrology, and Education
Volume: s 2 and 3
Subject Category
Numerical Analysis
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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