Differential evolution and quantum-inquired differential evolution for evolving Takagi-Sugeno fuzzy models
The differential evolution (DE) is a global optimization algorithm to solve numerical optimization problems. Recently the quantum-inquired differential evolution (QDE) has been proposed for binary optimization. This paper proposes DE/QDE to learn the Takagi-Sugeno (T-S) fuzzy model. DE/QDE can simultaneously optimize the structure and the parameters of the model. Moreover a new encoding scheme is given to allow DE/QDE to be easily performed. The two benchmark problems are used to validate the performance of DE/QDE. Compared to some existing methods. DE/QDE shows the competitive performance in terms of accuracy. (c) 2010 Elsevier Ltd. All rights reserved.
differential evolution quantum-inquired differential evolution takagi-sugeno fuzzy model identification