Elsevier

Applied Mathematical Modelling

Volume 48, August 2017, Pages 635-654
Applied Mathematical Modelling

Takagi–Sugeno fuzzy modelling of some nonlinear problems using ant colony programming

https://doi.org/10.1016/j.apm.2017.04.019Get rights and content
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Highlights

  • Fuzzy modeling of optimal control problem.

  • Nonlinear matrix Riccati differential equation is solved using ant colony programming.

  • The ant colony solution is an analytical and better solution than existing solutions.

Abstract

In this paper, the Takagi–Sugeno fuzzy model is derived from the given nonlinear systems. The objective is to linearize these nonlinear systems into several fuzzy differential equations according to the Takagi–Sugeno fuzzy rules. The present work implemented the nontraditional ant colony programming (ACP) method to solve these fuzzy differential equations. The proposed ACP algorithm manages to give either similar or almost close solutions to the analytical form. Accuracy of the solution computed by this ACP method is qualitatively better when it is compared with other nontraditional approaches such as the genetic programming (GP) method. Illustrative numerical examples and tables are presented for comparative purpose.

Keywords

Ant colony programming
Differential equation
Fuzzy modelling

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