Article ID Journal Published Year Pages File Type
407189 Neurocomputing 2016 6 Pages PDF
Abstract

This paper investigates the problem of dissipativity analysis for discrete-time fuzzy neural network with parameter uncertainties based on interval type-2 (IT2) fuzzy model. The parameter uncertainties are handled via the lower and upper membership functions. The original sufficient conditions are presented by a set of linear matrix inequalities (LMIs) to guarantee the dissipativity of the resulting system. The main contribution of this paper is that the discrete-time form of the IT2 T–S fuzzy neural network with leakage and time-varying delays is first proposed. Finally, a numerical example is provided to testify the effectiveness of the proposed results.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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