Optimization of Gaussian fuzzy membership functions and evaluation of the monotonicity property of Fuzzy Inference Systems

Tay, Kai Meng; Lim, Chee Peng

doi:10.1109/fuzzy.2011.6007387

Cited by 20 publications

(7 citation statements)

References 13 publications

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“…In this paper, the fulfillment of the monotonicity property is measured or evaluated by comparing the output pairs of an FIS model [10]. The monotonicity index serves as an approximate indicator whether an FIS model observes the monotonicity property or not.…”

Section: A Review On the Monotonicity Indexmentioning

confidence: 99%

“…Recent investigations on the monotone fuzzy modeling problem aim to develop a set of mathematical conditions as the governing equations [1,4,8,9], to apply the developed mathematical conditions to real-world problems [5][6][7], and to further extend or synthesize the mathematical conditions to/or with some advanced FIS modeling techniques [2,[10][11]. From the literatures, the mathematical conditions or guidelines for the Sugeno FIS model [1], Mamdani FIS model [8], and SIRM FIS model [4] have been developed.…”

Section: Introductionmentioning

confidence: 99%

“…It includes the construction of a type-1 or type-2 FIS model either manually or with machine learning techniques to generate/tune/optimize an FIS model [10] and to develop a new mathematical framework to facilitate/complement some FIS modeling procedures (e.g., similarity reasoning [11], re-labeling [12], operator and etc), by considering the monotonicity/local monotonicity relationship among the input(s) and the output of a system as additional qualitative information (or prior knowledge).…”

Section: Introductionmentioning

confidence: 99%

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A monotonicity index for the monotone fuzzy modeling problem

Tay¹,

Lim²,

Teh³

et al. 2012

2012 IEEE International Conference on Fuzzy Systems

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In this paper, the problem of maintaining the (global) monotonicity and local monotonicity properties between the input(s) and the output of an FIS model is addressed. This is known as the monotone fuzzy modeling problem. In our previous work, this problem has been tackled by developing some mathematical conditions for an FIS model to observe the monotonicity property. These mathematical conditions are used as a set of governing equations for undertaking FIS modeling problems, and have been extended to some advanced FIS modeling techniques. Here, we examine an alternative to the monotone fuzzy modeling problem by introducing a monotonicity index. The monotonicity index is employed as an approximate indicator to measure the fulfillment of an FIS model to the monotonicity property. It allows the FIS model to be constructed using an optimization method, or be tuned to achieve a better performance, without knowing the exact mathematical conditions of the FIS model to satisfy the monotonicity property. Besides, the monotonicity index can be extended to FIS modeling that involves the local monotonicity problem. We also analyze the relationship between the FIS model and its monotonicity property fulfillment, as well as derived mathematical conditions, using the Monte Carlo method.

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Section: A Review On the Monotonicity Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A monotonicity index for the monotone fuzzy modeling problem

Tay¹,

Lim²,

Teh³

et al. 2012

2012 IEEE International Conference on Fuzzy Systems

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“…In this section, a monotonicity test (Tay and Lim 2011b;Tay et al, 2012a,b) is used to evaluate whether the FIS-based RFN model satisfies the monotonicity property. The FIS-based RPN model is a three-input FIS model, i.e., FRPN ¼ f ð xÞ, where x ¼ ðS; O; DÞ.…”

Section: A Monotonicity Testmentioning

confidence: 99%

Application of the fuzzy Failure Mode and Effect Analysis methodology to edible bird nest processing

Jong¹,

Tay²,

Lim³

2013

Computers and Electronics in Agriculture

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. b s t r a c tThe focus of this paper is on the production processes of Edible Bird Nest (EBN) in Sarawak, Malaysia. Sarawak and Sabah (two states of Malaysia in the Borneo Island) are known as the second ranked resource area (after Indonesia) of the world for EBN production. In spite of the popularity of EBN as a food source and the important economic status of the EBN industry, the use of a quality and risk assessment tool for the production of EBN is new. As such, the implementation of an advanced quality and risk assessment tool, i.e., the fuzzy Failure Mode and Effect Analysis (FMEA) methodology, for EBN processing is described in this paper. Data and information are gathered from several EBN production sites, and fuzzy FMEA is adopted to analyze the collected data/information. It is worth mentioning that the EBN production in Sarawak is relatively traditional. As such, this work makes an important contribution to modernization of the EBN production industry in Sarawak, i.e., to improve the production process and ensure the quality of EBN via the use of a formal quality and risk assessment tool. Besides, this paper contributes to a new application of fuzzy FMEA to the agriculture and food domain.

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“…In this research, Gaussian-typed MF is implemented because its constructs match with the two outputs produced by FCM as mentioned above. Gaussian fuzzy sets used in input MFs is based on (2) as the following [27], [28]:…”

Section: Fis Constructionmentioning

confidence: 99%

A Comparative Study of Mamdani and Sugeno Fuzzy Models for Quality of Web Services Monitoring

Hasan¹,

Aziz²,

Jaafar³

et al. 2017

ijacsa

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Abstract-This paper presents a comparative study of fuzzy inference system (FIS) with respect to Mamdani and Sugeno FISs to show the accuracy and precision of quality of web service (QoWS) compliance monitoring. We used these two types of FIS for designing the QoWS compliance monitoring model. Clustering validity index is used to optimize the number of clusters of both models. Then both models are constructed based on Fuzzy CMeans (FCM) clustering algorithm. Simulation results with a Mamdani model, a Sugeno model and a crisp-based model for benchmark are presented. We consider different levels of noise (to represent uncertainties) in the simulations for comparison and to analyze the performance of the models when applied in QoWS compliance monitoring. The results show that Sugeno FIS outperforms Mamdani FIS in terms of accuracy and precision by producing better total error, error percentage, precision, mean squared error and root mean squared error measurements. The advantage of using fuzzy-based model is also verified with benchmark model.

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Optimization of Gaussian fuzzy membership functions and evaluation of the monotonicity property of Fuzzy Inference Systems

Cited by 20 publications

References 13 publications

A monotonicity index for the monotone fuzzy modeling problem

A monotonicity index for the monotone fuzzy modeling problem

Application of the fuzzy Failure Mode and Effect Analysis methodology to edible bird nest processing

A Comparative Study of Mamdani and Sugeno Fuzzy Models for Quality of Web Services Monitoring

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