Analysis of techniques for anfis rule-base minimization and accuracy maximization
Despite of acquiring popularity among researchers, the implementations of ANFIS-based models face problems when the number of rules surge dramatically and increase the network complexity, which consequently adds computational cost. Essentially, not all the rules in ANFIS knowledge-base are the poten...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/7179/ http://eprints.uthm.edu.my/7179/1/IC3E_2015_submission_089.pdf |
| Summary: | Despite of acquiring popularity among researchers, the implementations of ANFIS-based models face problems when the
number of rules surge dramatically and increase the network complexity, which consequently adds computational cost.
Essentially, not all the rules in ANFIS knowledge-base are the potential ones. They contain those rules which have either
minor or no contribution to overall decision. Thus, removing such rules will not only reduce complexity of the network, but
also cut computational cost. Thus, there are various rule-base optimization techniques, proposed in literature, which are
presented in motivation to simultaneously obtain rule-base minimization and accuracy maximization. This paper analyzes
some of those approaches and important issues related to achieving both the contradictory objectives simultaneously. In this
paper, Hyperplane Clustering, Subtractive Clustering, and the approach based on selecting and pruning rules are analyzed in
terms of optimizing ANFIS rule-base. The optimized rule-base is observed in connection with providing high accuracy. The
results and analysis, presented in this paper, suggest that the clustering approaches are proficient in minimizing ANFIS rulebase
with maximum accuracy. Although, other approaches, like putting threshold on rules’ firing strength, can also be
improved using metaheuristic algorithms. |
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