A Novel Adaptive Fuzzy Inference System for Mobile Robot Navigation


Hossen, Md. Jakir and Sayeed, Md. Shohel and Muhamad Amin, Anang Hudaya and Abdullah, Mohd Fikri Azli and Yusof, Ibrahim (2013) A Novel Adaptive Fuzzy Inference System for Mobile Robot Navigation. Journal of Theoretical and Applied Information Technology, 57 (3). pp. 569-578. ISSN 1992-8645

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The Fuzzy hybridization technique for intelligent systems have become of research interests in a variety of research areas over the past decade. There are limitations faced by all popular fuzzy systems architectures when they are applied to applications with a large number of inputs (more than three). The present paper proposes a novel adaptive fuzzy inference system for multi-sensors mobile robot navigation. A novel fuzzy inference system is constructed by the automatic generation of membership functions (MFs) and formed a minimal numbers of rules using hybrid fuzzy clustering algorithm (Combination of Fuzzy C-means and Subtractive clustering algorithm) and the modified apriori algorithm, respectively. A modified apriori algorithm is utilized to count the number of common elements from the clusters and to obtain a minimal set of decision rules based on input-output datasets. The generated modified adaptive fuzzy inference system is then adjusted by the least square method and the gradient descent algorithm towards better performance with a minimal set of rules. The proposed algorithm is able to reduce the number of rules which increases exponentially when more input variables are involved. The performance is compared with other existing approaches in an application of mobile robot navigation and shown to be very competitive and improved results.

Item Type: Article
Uncontrolled Keywords: Apriori algorithm, Fuzzy C-means, Subtractive clustering, and TSK
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Information Science and Technology (FIST)
Faculty of Engineering and Technology (FET)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 13 Jan 2017 05:14
Last Modified: 13 Jan 2017 05:14
URII: http://shdl.mmu.edu.my/id/eprint/6122


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