First Asia International Conference on Modelling & Simulation (AMS'07) Recursive Least Square and Fuzzy Modelling Using Genetic Algorithm for Process Control Application Prince of Songkla University, Phuket, Thailand March 27-March 30 ISBN: 0-7695-2845-7
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AMS.2007.83
A technique for the modelling of nonlinear process control using Recursive Least Square and Takagi-Sugeno Fuzzy System with Genetic Algorithm topology is described. This paper discusses the identification of parameters of the fuzzy sets at the antecedent part and linear model at the consequent part of fuzzy model within an application to process control. The key issues of finding the best model of the process are described. Results show that fuzzy model with genetic algorithm gives minimum mean squared error compare with recursive least square.
Citation:
Ribhan Zafira Abdul Rahman, Rubiyah Yusof, Marzuki Khalid, "Recursive Least Square and Fuzzy Modelling Using Genetic Algorithm for Process Control Application," ams, pp.388-393, First Asia International Conference on Modelling & Simulation (AMS'07), 2007 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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