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 10 publications

(5 citation statements)

References 21 publications

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“…Unfortunately, there are significant drawbacks when considering Takagi-Sugeno fuzzy systems for regression with the most commonly used membership functions [43,44]. Triangular or trapezoidal membership functions produce non-smooth output whereas monotonicity conditions for Gaussian membership functions [26] are very conservative due to unbounded support.…”

Section: Introductionmentioning

confidence: 99%

Monotonic Fuzzy Systems With Goniometric Membership Functions

Hušek

2024

Int. J. Fuzzy Syst.

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Fuzzy logic-based systems are nowadays commonly used in nonlinear function approximation when incoming data are available. Their main advantage is that the resulting rules can be interpreted understandably. Nevertheless, when the data are noisy an overfitting may occur which leads to poor accuracy and generalization ability. Prior information about the nonlinear function may improve fuzzy system performance. In this paper the case when the function is monotonic with respect to some or all variables is considered. Sufficient conditions for the monotonicity of first-order Takagi–Sugeno fuzzy systems with raised cosine membership functions are derived. Performance of the proposed fuzzy system is tested on two benchmark datasets

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Section: Introductionmentioning

confidence: 99%

Monotonic Fuzzy Systems With Goniometric Membership Functions

Hušek

2024

Int. J. Fuzzy Syst.

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

Cited by 10 publications

References 21 publications

Monotonic Fuzzy Systems With Goniometric Membership Functions

Monotonic Fuzzy Systems With Goniometric Membership Functions

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

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

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