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Optimizing Nonlinear Parameters of Sugeno Type Fuzzy Rules using GWO for Data Classification

Musbah Abdulgader

Department of Electrical Engineering and Computer Science, University of Toledo, Toledo, OH 34607, USA

E-mail Address: musbah.abdulgader@rockets.utoledo.edu

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and

Devinder Kaur

Department of Electrical Engineering and Computer Science, University of Toledo, Toledo, OH 34607, USA

E-mail Address: devinder.kaur@utoledo.edu

Corresponding author.

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https://doi.org/10.1142/S1469026820500091Cited by:0 (Source: Crossref)

Abstract

In this paper, a Sugeno type fuzzy system based on the fuzzy clustering has been developed for a variety of datasets. The number of rules for each dataset is based on the optimum number of clusters in that dataset. Rule sets provide the knowledge base for the classification of data. Each rule set is fine-tuned using the GWO with the intention to improve the classification. The approach is compared with the work of previous researchers on similar data sets using a variety of techniques, including nature-inspired algorithms such as genetic algorithms and Swarm based algorithms. Statistical Analysis of the performance of GWO shows that it is better than five other algorithms 95% of the time.

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