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A modified neuro-fuzzy system using metaheuristic approaches for data classification

Mohd Salleh, Mohd Najib and Talpur, Noureen and Talpur, Kashif Hussain (2018) A modified neuro-fuzzy system using metaheuristic approaches for data classification. In: Artificial Intelligence - Emerging Trends and Applications. IntechOpen, pp. 29-45. ISBN 9781789233650

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The impact of innovated Neuro-Fuzzy System (NFS) has emerged as a dominant technique for addressing various difficult research problems in business. ANFIS (Adaptive Neuro-Fuzzy Inference system) is an efficient combination of ANN and fuzzy logic for modeling highly non-linear, complex and dynamic systems. It has been proved that, with proper number of rules, an ANFIS system is able to approximate every plant. Even though it has been widely used, ANFIS has a major drawback of computational complexities. The number of rules and its tunable parameters increase exponentially when the numbers of inputs are large. Moreover, the standard learning process of ANFIS involves gradient based learning which has prone to fall in local minima. Many researchers have used meta-heuristic algorithms to tune parameters of ANFIS. This study will modify ANFIS architecture to reduce its complexity and improve the accuracy of classification problems. The experiments are carried out by trying different types and shapes of membership functions and meta-heuristics Artificial Bee Colony (ABC) algorithm with ANFIS and the training error results are measured for each combination. The results showed that modified ANFIS combined with ABC method provides better training error results than common ANFIS model.

Item Type: Book Section
Uncontrolled Keywords: Adaptive neuro-fuzzy inference system; meta-heuristics; classification problem
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 29 Aug 2019 03:47
Last Modified: 29 Aug 2019 03:47
URI: http://eprints.uthm.edu.my/id/eprint/11522
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