Balanced the Trade-offs Problem of ANFIS using Particle Swarm Optimization
Abstract: Improving the
approximation accuracy and interpretability of fuzzy systems is an important
issue either in fuzzy systems theory or in its applications . It is known that
simultaneous optimization both issues was the trade-offs problem, but it will
improve performance of the system and avoid overtraining of data. Particle
swarm optimization (PSO) is part of evolutionary algorithm that is good
candidate algorithms to solve multiple optimal solution and better global
search space. This paper introduces an integration of PSO dan ANFIS for
optimise its learning especially for tuning membership function parameters and
finding the optimal rule for better classification. The proposed method has
been tested on four standard dataset from UCI machine learning i.e. Iris
Flower, Haberman’s Survival Data, Balloon and Thyroid dataset. The results have
shown better classification using the proposed PSO-ANFIS and the time
complexity has reduced accordingly.
Author: Dian Palupi Rini, Siti
Mariyam Shamsuddin, Siti Sophiayati Yuhaniz
Journal Code: jptkomputergg130091