Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm

Saima Hassan*, Mojtaba Ahmadieh Khanesar, Jafreezal Jaafar, Abbas Khosravi

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


An optimized design of a fuzzy logic system can be regarded as setting of different parameters of the system automatically. For a single parameter, there may exist multiple feasible values. Consequently, with the increase in number of parameters, the complexity of a system increases. Type 2 fuzzy logic system has more parameters than the type 1 fuzzy logic system and is therefore much more complex than its counterpart. This paper proposes optimal parameters for an extreme learning machine-based interval type 2 fuzzy logic system to learn its best configuration. Extreme learning machine (ELM) is utilized to tune the consequent parameters of the interval type 2 fuzzy logic system (IT2FLS). A disadvantage of ELM is the random generation of its hidden neuron that causes additional uncertainty, in both approximation and learning. In order to overcome this limitation in an ELM-based IT2FLS, artificial bee colony optimization algorithm is utilized to obtain its antecedent parts parameters. The simulation results verified better performance of the proposed IT2FLS over other models with the benchmark data sets.

Original languageEnglish
JournalNeural Computing and Applications
Pages (from-to)1-14
Number of pages14
Publication statusPublished - 1 Aug 2016
Externally publishedYes


  • Artificial bee colony
  • Extreme learning machine
  • Hybrid learning
  • Interval type 2 fuzzy logic systems
  • Optimal parameters


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