Fuzzy rule based decision trees

Wang, Xianchang; Liu, Xiaodong; Pedrycz, Witold; Zhang, Lishi

doi:10.1016/j.patcog.2014.08.001

Cited by 72 publications

(31 citation statements)

References 31 publications

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“…If the function v(t) lies in the sector [β, β + k], then using the substitution (7) we obtain the equivalent system (see "eigenvalues shifting" in [32], [33]) (9). Applying Theorem 1 for G 1 (s) we get the condition (12) in Theorem 2.…”

Section: Appendix a Proof Of Theoremmentioning

confidence: 99%

“…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%

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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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Section: Appendix a Proof Of Theoremmentioning

confidence: 99%

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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“…However, most of the association rule mining methods did not consider the ambiguity of data in the process of data pre-processing stage that cannot reflect the representative of data and human's cognition completely and truly (Kuok et al, 1998;Kaya & Alhajj, 2003;Weng, 2011;Matthews et al, 2013;Wang et al, 2015). Therefore, converting the data into linguistic data by using fuzzy sets has become an important research direction for the data mining application (Ashish & Vikramkumar, 2010;Weng, 2011;Sowan et al, 2013;Jin et al, 2014;Arafah & Mukhlash, 2015;Palacios et al, 2015;Khatib et al, 2015).…”

Section: Contribution Of This Paper To the Literaturementioning

confidence: 99%

A Study on the Rare Factors Exploration of Learning Effectiveness by Using Fuzzy Data Mining

Chen

Chang

2017

EURASIA J MATH SCI T

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The phenomenon of low fertility has been negative impacted on the social structure of the educational environment in Taiwan. To increase the learning effectiveness of students became the most important issue for the Universities in Taiwan. Due to the subjective judgment of evaluators and the attributes of influenced factors are always fuzzy, it is not easy to measure the learning effectiveness by using the crisp values for the qualitative factors. In additional, the negative behaviors or the infrequent learning data (rare item sets) cannot easily excavate from educational database. Therefore, a systematic mining method is proposed here by combining fuzzy set theory with data mining technology to explore the key factors of learning effectiveness from the infrequent data. A case study implemented in this paper and showed that the proposed method can effectively find the important factors and valuable patterns from the survey data. The results of this paper can provide the important information for teachers to help students increase the learning effectiveness. Finally, the conclusions are addressed at the end of this paper.

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“…Table 2 lists the category which the event belongs to. Classification Tree (CT) [24] and Support Vector Machine (SVM) [11,21] are the famous methods for classification. CT makes the decision tree to the discrete variable.…”

Section: Supervised Anomaly Detection---classification and Regressionmentioning

confidence: 99%

There is a Gold Mine in Flight Data: A Framework of Data Mining in Civil Aviation

Zhao¹,

Li²,

Wang³

2017

dtssehs

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Abstract. Preventing unsafe incidents is the focus of civil aviation. Flight data monitoring, also referred to as Flight Operations Quality Assurance (FOQA) is a powerful supporting tool and means for data collection and analysis. FOQA is regularly implemented in civil aviation where the application of data mining methods has been proposed. This paper surveys data mining techniques and aims at the beginner with little foundations of data mining or civil aviation, as well as a review of published work on application to flight data. It then illustrates that flight data is exactly a gold mine, and the application of data mining in FOQA is an opportunity as well as a challenge.

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Fuzzy rule based decision trees

Cited by 72 publications

References 31 publications

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

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

A Study on the Rare Factors Exploration of Learning Effectiveness by Using Fuzzy Data Mining

There is a Gold Mine in Flight Data: A Framework of Data Mining in Civil Aviation

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