DocumentCode :
3601349
Title :
An Evolving Interval Type-2 Neurofuzzy Inference System and Its Metacognitive Sequential Learning Algorithm
Author :
Das, Ankit Kumar ; Subramanian, Kartick ; Sundaram, Suresh
Author_Institution :
Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore
Volume :
23
Issue :
6
fYear :
2015
Firstpage :
2080
Lastpage :
2093
Abstract :
In this paper, we propose an evolving interval type-2 neurofuzzy inference system (IT2FIS) and its fully sequential learning algorithm. IT2FIS employs interval type-2 fuzzy sets in the antecedent part of each rule and the consequent realizes Takagi-Sugeno-Kang fuzzy inference mechanism. In order to render the inference fast and accurate, we propose a data-driven interval-reduction approach to convert interval type-1 fuzzy set in antecedent to type-1 fuzzy number in the consequent. During learning, the sequential algorithm learns a sample one-by-one and only once. The IT2FIS structure evolves automatically and adapts its network parameters using metacognitive learning mechanism concurrently. The metacognitive learning regulates the learning process by appropriate selection of learning strategies 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 type-2 neurofuzzy inference system (McIT2FIS) is evaluated using a set of benchmark time-series problems and is compared with existing type-2 and type-1 fuzzy inference systems. Finally, the performance of the proposed McIT2FIS has been evaluated using a practical stock price-tracking problem. The results clearly highlight that McIT2FIS performs better than other existing results in the literature.
Keywords :
cognitive systems; evolutionary computation; fuzzy neural nets; fuzzy set theory; learning (artificial intelligence); McIT2FIS; McTI2FIS; Takagi-Sugeno-Kang fuzzy inference mechanism; benchmark time-series problems; data-driven interval-reduction approach; evolving interval Type-2 neurofuzzy inference system; fully sequential learning algorithm; fuzzy rule; input-output relationship; interval type-2 fuzzy sets; learning strategy selection; metacognitive learning mechanism; metacognitive sequential learning algorithm; network parameter adaptation; sample learning; stock price-tracking problem; type-1 fuzzy number; Approximation algorithms; Clustering algorithms; Fuzzy logic; Fuzzy sets; Inference algorithms; Knowledge engineering; Uncertainty; Adaptive Interval Reduction; Adaptive interval reduction; Interval Type-2 Fuzzy Set; Meta-Cognition; Neuro-Fuzzy Inference System; Sequential Learning; interval type-2 fuzzy set; metacognition; neurofuzzy inference system; sequential learning;
fLanguage :
English
Journal_Title :
Fuzzy Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
1063-6706
Type :
jour
DOI :
10.1109/TFUZZ.2015.2403793
Filename :
7042305
Link To Document :
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