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Membership function

About: Membership function is a research topic. Over the lifetime, 15795 publications have been published within this topic receiving 418366 citations.


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TL;DR: It is demonstrated how shadowed sets help in problems in data interpretation in fuzzy clustering by leading to the three-valued quantification of data structure that consists of core, shadowed, and uncertain structure.
Abstract: In this study, we discuss a concept of shadowed sets and present their applications. To establish some sound compromise between the qualitative Boolean (two-valued) description of data and quantitative membership grades, we introduce an interpretation framework of shadowed sets. Shadowed sets are discussed as three-valued constructs induced by fuzzy sets assuming three values (that could be interpreted as full membership, full exclusion, and uncertain membership). The algorithm of converting membership functions into this quantification is a result of a certain optimization problem guided by the principle of uncertainty localization. We revisit fundamental ideas of relational calculus in the setting of shadowed sets. We demonstrate how shadowed sets help in problems in data interpretation in fuzzy clustering by leading to the three-valued quantification of data structure that consists of core, shadowed, and uncertain structure. © 2008 Wiley Periodicals, Inc.

112 citations

Journal ArticleDOI
TL;DR: Neuro-Fuzzy Inference System adopted on a Takagi-Sugeno-Kang (TSK) type Fuzzy Rule Based System is developed for stock price prediction and this index with high accuracy near by 97.8% has successfully forecasted with several experimental tests from different sectors.
Abstract: Nowadays because of the complicated nature of making decision in stock market and making real-time strategy for buying and selling stock via portfolio selection and maintenance, many research papers has involved stock price prediction issue Low accuracy resulted by models may increase trade cost such as commission cost in more sequenced buy and sell signals because of insignificant alarms and otherwise bad diagnosis in price trend do not satisfy trader's expectation and may involved him/her in irrecoverable cost Therefore, in this paper, Neuro-Fuzzy Inference System adopted on a Takagi-Sugeno-Kang (TSK) type Fuzzy Rule Based System is developed for stock price prediction The TSK fuzzy model applies the technical index as the input variables and the consequent part is a linear combination of the input variables Fuzzy C-Mean clustering implemented for identifying number of rules Initial membership function of the premise part approximately defined as Gaussian function TSK parameters tuned by Adaptive Nero-Fuzzy Inference System (ANFIS) Proposed model is tested on the Tehran Stock Exchange Indexes (TEPIX) This index with high accuracy near by 978% has successfully forecasted with several experimental tests from different sectors

112 citations

Journal ArticleDOI
TL;DR: This paper presents a general framework for the study of relation-based (I,T)-intuitionistic fuzzy rough sets by using constructive and axiomatic approaches and different axiom sets characterizing the essential properties of intuitionism fuzzy approximation operators associated with various intuitionistic fuzzy relations.

112 citations

Journal ArticleDOI
14 Jun 2006
TL;DR: A GA-based framework for finding membership functions suitable for mining problems and then using the final best set of membership functions to mine fuzzy association rules, which shows the effectiveness of the framework.
Abstract: Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. This paper thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. We present a GA-based framework for finding membership functions suitable for mining problems and then use the final best set of membership functions to mine fuzzy association rules. The fitness of each chromosome is evaluated by the number of large 1-itemsets generated from part of the previously proposed fuzzy mining algorithm and by the suitability of the membership functions. Experimental results also show the effectiveness of the framework.

112 citations

Journal ArticleDOI
TL;DR: An intelligent second-order sliding-mode control using a wavelet fuzzy neural network with an asymmetric membership function (WFNN-AMF) estimator is proposed in this study to control a six-phase permanent magnet synchronous motor for an electric power steering (EPS) system.
Abstract: An intelligent second-order sliding-mode control (I2OSMC) using a wavelet fuzzy neural network with an asymmetric membership function (WFNN-AMF) estimator is proposed in this study to control a six-phase permanent magnet synchronous motor (PMSM) for an electric power steering (EPS) system. First, the dynamics of the steer-by-wire (SBW) EPS system and six-phase PMSM drive system with a lumped uncertainty are described in detail. Then, to alleviate the chattering phenomena in a traditional sliding-mode control (SMC), a second-order sliding-mode control (2OSMC) is designed. Moreover, the I2OSMC is developed to improve the required control performance of the EPS system. In the I2OSMC, the WFNN-AMF estimator with accurate approximation capability is employed to estimate the lumped uncertainty. Furthermore, the adaptive learning algorithms for the online training of the WFNN-AMF are derived using the Lyapunov theorem to guarantee the asymptotical stability of the closed-loop system. In addition, a 32-bit floating-point digital signal processor (DSP), i.e., TMS320F28335, is adopted for the implementation of the proposed control approach. Finally, some experimental results are illustrated to demonstrate the validity of the proposed I2OSMC using the WFNN-AMF estimator for the EPS system.

112 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202353
2022123
2021340
2020354
2019385
2018433