Current Proceedings on Technology

Yazarlar: Siti Mariyam Shamsuddin, Dian Palupi Rini, Siti Sophiayati Yuhaniz

Konular:-

Anahtar Kelimeler:ANFIS,Interpretability,Accuracy,Evolutionary algorithms,Particle Swarm Optimisation

Özet: 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 optimisation both issues was the trade-offs problem, but it will improve performance of the system and avoid overtraining of data. Particle swarm optimisation (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 for optimising the ANFIS 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.

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