An Evolving Interval Type-2 Neurofuzzy Inference System and Its Metacognitive Sequential Learning Algorithm


During this paper, we tend to propose an evolving interval type-two neurofuzzy inference system (IT2FIS) and its fully sequential learning algorithm. IT2FIS employs interval kind-2 fuzzy sets within the antecedent half of every rule and the consequent realizes Takagi–Sugeno–Kang fuzzy inference mechanism. In order to render the inference fast and correct, we tend to propose a information-driven interval-reduction approach to convert interval kind-1 fuzzy set in antecedent to type-one fuzzy number in the resultant. During learning, the sequential algorithm learns a sample one-by-one and solely once. The IT2FIS structure evolves automatically and adapts its network parameters using metacognitive learning mechanism concurrently. The metacognitive learning regulates the educational process by applicable choice of learning ways and helps the proposed IT2FIS to approximate the input–output relationship efficiently. An evolving IT2FIS employing a metacognitive learning algorithm is referred to as McTI2FIS. Performance of metacognitive interval kind-2 neurofuzzy inference system (McIT2FIS) is evaluated employing a set of benchmark time-series problems and is compared with existing kind-2 and sort-one fuzzy inference systems. Finally, the performance of the proposed McIT2FIS has been evaluated using a sensible stock worth-tracking drawback. The results clearly highlight that McIT2FIS performs better than other existing ends up in the literature.

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