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Fuzzy Min Max Neural Network for pattern classification: An overview of complexity problem

Al Sayaydeh, Osama Nayel and Shamaileh, Abeer (2018) Fuzzy Min Max Neural Network for pattern classification: An overview of complexity problem. International Journal of Information Technology and Language Studies (IJITLS), 2 (3). pp. 110-117. ISSN 2521-8727

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Over the last years, the pattern classification is considered one of the most significant domains in artificial intelligence (AI), because it shapes a fundamental in many diverse real live applications where the artificial neural networks (ANNs) and fuzzy logic (FL) are most extensively utilized in pattern classification. In order to construct an effective and robust classifier, researchers have invented hybrid systems that combine both FL and ANNs. The Fuzzy Min Max (FMM) neural network has been proven to be a robust classifier for handling pattern classification issues. Although FMM has several features, it suffers from several limitations. Thus, researchers have introduced a lot of improvements to beat the shortcomings of FMM neural network. This paper focuses on a complete review of developments carried out on FMM neural network for addressing the complexity problem in order to help new researchers in identifying the recent strategies used to address the complexity problem.

Item Type: Article
Uncontrolled Keywords: FMM, Pattern Classification, online learning, neural network.
Subjects: Q Science > QA Mathematics
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Noorul Farina Arifin
Date Deposited: 28 Jan 2019 01:01
Last Modified: 28 Jan 2019 01:01
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