Design and implementation of a fuzzy inference system for supporting customer requirements

Juang, Ying-Shen; Lin, Shui-Shun; Kao, Hsing-Pei

doi:10.1016/j.eswa.2006.01.052

Cited by 30 publications

(7 citation statements)

References 10 publications

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“…Just as the FDEV approach [16], but not limited to fault diagnosis, it is generic and can describe many systems, but it offers less functionality those business methods or that dedicated software [17], [18], [19], [20], [21], its main advantage is of being coupled and integrated with DEVS formalism, thereby making the decision support or control (inference) with the simulation. Besides, many improvements are underway.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy inference models for Discrete EVent systems

Capocchi¹,

Bisgambiglia²,

Garredu³

2010

International Conference on Fuzzy Systems

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For several years, we worked to improve a discrete events modeling formalism: called DEVS. Having defined a method to take into account the inaccuracies iDEVS, in this paper, we present the second part of our research work.Generally, our approach is to associate the DEVS formalism with an object class, which allows using it to new fields of study, and in our case fuzzy systems.This paper describes a new modeling methodology. It allows to modeling and to use fuzzy inference systems (FIS) with DEVS formalism in order to perform the control or the learning on systems described incompletely or with linguistic data. The advantages of this method are numerous: to extend the DEVS formalism to other application fields; to propose new DEVS models for fuzzy inference; to provide users with simple and intuitive modeling methods. Throughout this paper we describe the tools and methods which were developed to make possible the combination of these two approaches.

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Section: Discussionmentioning

confidence: 99%

Fuzzy inference models for Discrete EVent systems

Capocchi¹,

Bisgambiglia²,

Garredu³

2010

International Conference on Fuzzy Systems

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“…Globally there are many research works proposing an implementation of FIS [13,14,15]. Despite the wealth and quality of literature in the eld, none of these works fully meet our expectations and goals.…”

Section: Figure 1: Structure Of Optimize Fuzzy Inference Systemmentioning

confidence: 94%

“…Despite, there are many frameworks that provide tools to model FIS [13,16,22,14,15], and the quality of literature in this eld. Some tools do not allow for simulation: FisPro, other do not make discrete simulation: MatLab.…”

Section: Discussion and Remarksmentioning

confidence: 99%

Models-based Optimization Methods for the Specication of Fuzzy Inference Systems in Discrete EVent Simulation

Bisgambiglia¹,

Poggi²,

Nicolai³

2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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Fuzzy Inference Systems (FIS) have the advantage of relying on the properties of Fuzzy Logic to represent imperfect information so gradually, and manipulate them from a linguistic description. This exibility of representation is more signicant for the study of complex systems. Our aims are to propose a formal approach for describing FIS as a Discrete Event System (DES), and to extend a DES in order to use the many advantages oered by FIS: exibility, easy implementation, robustness... In this paper, we present the extension of Discrete EVent system Specication (DEVS) formalism to represent FIS, and we propose a modular approach (DEV-FIS) to use several optimization methods. We focus mainly on the used new aproach about using genetic algoritm in order to optimize the FIS.

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“…FIS has been used in many applications since then. It has been applied in environmental models including: waste management (Vesely et al, 2016) forecasting air quality (Carbajal-Hernández et al, 2012a;Fisher, 2006;Sowlat et al, 2011) water quality (Carbajal-Hernández et al, 2012b;Che Osmi et al, 2016;Gharibi et al, 2012;Ocampo-Duque et al, 2006) models for performing risk assessment (Camastra et al, 2015;Jamshidi et al, 2013;Rodríguez et al, 2016) in the field of manufacturing and sales for supporting customers' requirements (Juang et al, 2007) forecasting automobile sales (Wang et al, 2011) stock price prediction (Chang & Liu, 2008) supplier selection (Lima Junior et al, 2013) measuring customer satisfaction (Zani et al, 2013). Similarly to our model, FIS has been applied in models for evaluating the performance level of several fields (Nadali et al, 2011;Nilashi et al, 2015).…”

Section: A Fuzzy Logic Approachmentioning

confidence: 99%