Fuzzy data mining and management of interpretable and subjective information

Marsala, Christophe; Bouchon‐Meunier, Bernadette

doi:10.1016/j.fss.2015.08.021

Cited by 12 publications

(2 citation statements)

References 40 publications

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“…DM is recognized as an iterative process within which the progress is defined by the discovery of various relationships, through either automatic or manual methods (Lin, Chiu, Huang, & Yen, 2015). DM is a process that is based on the application of machine learning algorithm in a given application task (Marsala & Bouchon-Meunier, 2015). Sparks et al (2016) indicated that machine learning algorithms are the popular DM tools, but require the large training sets to be effective.…”

Section: Overview Of Data Miningmentioning

confidence: 99%

Multifaceted Applications of Data Mining, Business Intelligence, and Knowledge Management

Kasemsap

2016

International Journal of Social and Organizational Dynamics in IT

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This article reviews the literature in the search for the multifaceted applications of data mining (DM), business intelligence (BI), and knowledge management (KM). The literature review highlights the overviews of DM, BI, and KM; the practical applications of DM, BI, and KM; and the prospects of DM, BI, and KM in terms of marketing, business, human resources, and manufacturing. DM plays a key role in organizing huge amount of data and condensing it into valuable information. BI involves the delivery and integration of relevant and useful business information in an organization. KM allows companies to manage a system of core competencies in order to maximize business opportunities and minimize the risk of losing business opportunities. The findings present valuable insights and further understanding of the way in which DM, BI, and KM efforts should be focused.

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Section: Overview Of Data Miningmentioning

confidence: 99%

Multifaceted Applications of Data Mining, Business Intelligence, and Knowledge Management

Kasemsap

2016

International Journal of Social and Organizational Dynamics in IT

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“…Fuzzy rule-based systems have been applied in various applications, for example in automatic control and robotics [1]- [4], ambient intelligence [5]- [7], computer vision [8]- [10], decision making [11], [12] or data mining [13], [14]. In all of these areas, the proper selection of fuzzy rules is important, especially in control where they determine the stability and quality of the system.…”

Section: Introductionmentioning

confidence: 99%

Design of State Feedback Adaptive Fuzzy Controllers for Second-Order Systems Using a Frequency Stability Criterion

Wiktorowicz

2017

IEEE Trans. Fuzzy Syst.

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This paper proposes a design method for secondorder systems that guarantees the stability of an adaptive fuzzy controller with state feedback. The system consists of a linear unstable plant, the Takagi-Sugeno controller, a nonlinear reference model and an adaptation mechanism. In this paper, gradient-based adaptation is used to change the consequents of the controller rules so that the closed-loop system behaves like the reference model. The proposed method utilizes a frequencydomain criterion in the form of the modified circle theorem. The controller function is assumed to be a nonlinearity described by a sector condition, which means that the function lies between two planes. During the process of adaptation this function is verified so it stays in the sector guaranteeing stability. The method described here is illustrated by an example of a control system containing an unstable plant with an unknown pole in the right half-plane. The motivation of this paper is to propose a frequency-domain method for the design of state feedback adaptive fuzzy controllers. Comparing with time domain methods that require advanced software tools, the proposed method offers simple graphical interpretation on the Nyquist plane.Index Terms-adaptive control, fuzzy control, nonlinear control systems, stability NOMENCLATURE r(t)Reference signal. v(t)Output of the controller. u(t) Control variable. x(t)State vector of the plant. x m (t)State vector of the reference model. ε(t) Adaptation error. A, BMatrices of the plant. A m , B m

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Information Quality: The Contribution of Fuzzy Methods

Bouchon‐Meunier

2020

Data Science for Financial Econometrics

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Fuzzy data mining and management of interpretable and subjective information

Cited by 12 publications

References 40 publications

Multifaceted Applications of Data Mining, Business Intelligence, and Knowledge Management

Multifaceted Applications of Data Mining, Business Intelligence, and Knowledge Management

Design of State Feedback Adaptive Fuzzy Controllers for Second-Order Systems Using a Frequency Stability Criterion

Information Quality: The Contribution of Fuzzy Methods

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