Derivative-Based Learning of Interval Type-2 Intuitionistic Fuzzy Logic Systems for Noisy Regression Problems

Eyoh, Imo; Umoh, Uduak; Inyang, Udoinyang G.; Eyoh, Jeremiah

doi:10.1007/s40815-020-00806-z

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…For example, in the adaptive inverse control method, it is necessary to have an exact inverse model, which T2F-NNs can be used Kien et al 2020 ; Tavoosi et al 2011b ; Zhao et al 2017 . In predicting the future of a dynamic system such as a stock market or a weather situation, a recurrent type-2 fuzzy system could be a good model for these purposes José Ángel Barrios 2020 ; Eyoh et al 2020 ; Narges Shafaei Bajestani 2017 . In terms of data segregation and classification, a precise type-2 fuzzy system can more accurately categorize and completely separate data, and this is very common in telecommunications.…”

Section: Introductionmentioning

confidence: 99%

A review on type-2 fuzzy neural networks for system identification

2021

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In many engineering problems, the systems dynamics are uncertain, and then, the accurate dynamic modeling is required. Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability. In this paper, the application of T2F-NNs is reviewed and classified. First, an introduction to the principles of system identification, including how to extract data from a system, persistency of excitation, preprocessing of information and data, removal of outlier data, and sorting of data to learn the T2F-NNs, is presented. Then, various learning methods for structure and parameters of the T2F-NNs are reviewed and analyzed. A number of different T2F-NNs that have been used to system identification are reviewed, and their disadvantages and advantages are described. Also, their efficiency in different applications is reviewed. Finally, we will look at the horizon ahead in this issue and analyze its challenges.

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Section: Introductionmentioning

confidence: 99%

A review on type-2 fuzzy neural networks for system identification

2021

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“…According to [16], intuitionistic fuzzy set (IFS) provides an efficient means of expressing a fuzzy set where available information is insufficient to define an imprecise concept using the traditional fuzzy sets. In the same vein, [17] pointed out that using IFS provides a more natural form of decision making where more than two answers are involved compared to traditional FLS. This work adopts the same dataset as presented in [11].…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic fuzzy Logic System and its Application to Global Carbon Dioxide Emissions Prediction

2021

IJATCSE

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In this study, a Takagi-Sugeno-Kang based intuitionistic fuzzy logic system is proposed for the prediction of global carbon dioxide emission for the first time. The intuitionistic fuzzy logic system is an integration of artificial neural network learning and intuitionistic fuzzy logic reasoning. The gradient descent back propagation is applied in the optimization of the parameters of the proposed model. The model is evaluated based on some performance metrics. Results of evaluation revealed that the intuitionistic fuzzy logic system outperforms other existing models in the literature in terms of prediction accuracy

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“…In this work therefore, an interval type-2 intuitionistic fuzzy logic system (IT2IFLS) is proposed for the first time to predict software fault at the requirement phase of SDLC. Intuitionistic fuzzy set (IFS) [33] proposed by Atanassov deals with uncertainty using degrees of membership function (MF) and non-membership function (NMF) of an element to a fuzzy set as well as hesitation index, hence it becomes the appropriate tool to deal with imprecise and vague information [34]. However, because the IFS of type-1 is designed around a single point of view and may not capture various forms of uncertainties, Eyoh et al [31] presented a rule-based interval type-2 intuitionistic fuzzy logic system (IT2IFLS) that are defined using fuzzy MFs and NMFs together with hesitation indexes.…”

Section: Introductionmentioning

confidence: 99%

Software Fault Prediction Based on Interval Type-2 Intuitionistic Fuzzy Logic System

Eyoh¹,

Udo²,

Umoeka³

2021

IJASRE

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With the continuous expansion and innovations in modern software development, the rate at which defects are present in software is directly proportional to how sophisticated and complex the software tends to be. Software fault prediction therefore continues to remain an important area of research in software engineering especially as new modeling algorithms are still emerging. In spite of the fact that the potential implementations of fuzzy set theory in software fault prediction have been explored in the past, to the best knowledge of the authors, it has not yet examined how interval type-2 intuitionistic fuzzy sets with membership and non-membership degrees could be used with parameter optimization in this domain. Therefore, this work aims to adopt an interval type-2 intuitionistic fuzzy logic system to predict fault in the requirement phase of Software Development Life Cycle. Intuitionistic fuzzy logic system deals with uncertainty using separate degrees of membership and non-membership of an element to a set as well as hesitation index, therefore becoming more appropriate and flexible tool to deal with imprecision and vagueness in data. Experimental analyses show that the obtained software prediction outputs using interval type-2 intuitionistic fuzzy logic system are very close to the actual outputs which confirm that the proposed approach is a more realistic alternative for software fault prediction.

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Derivative-Based Learning of Interval Type-2 Intuitionistic Fuzzy Logic Systems for Noisy Regression Problems

Cited by 8 publications

References 21 publications

A review on type-2 fuzzy neural networks for system identification

A review on type-2 fuzzy neural networks for system identification

Intuitionistic fuzzy Logic System and its Application to Global Carbon Dioxide Emissions Prediction

Software Fault Prediction Based on Interval Type-2 Intuitionistic Fuzzy Logic System

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