Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems

Starczewski, Janusz T.; Goetzen, Piotr; Napoli, Christian

doi:10.2478/jaiscr-2020-0018

Cited by 23 publications

(6 citation statements)

References 32 publications

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“…In cases where we have even a small portion of undistorted training data, we perform the initial training of type-2 systems by treating them as type-1 fuzzy systems, and then we use the method of generating type-2 fuzzy rules for uncertain data using the fuzzy-rough approximation [12,18,17] or possibilistic fuzzification [16,13]. So we reaffirm that type-2 fuzzy systems are key to extracting explanatory fuzzy rules, especially in the case of uncertainty or even ambivalency of these rules.…”

Section: Discussionmentioning

confidence: 99%

A Novel Approach to Type-Reduction and Design of Interval Type-2 Fuzzy Logic Systems

Starczewski

Przybyszewski

Byrski

et al. 2022

Journal of Artificial Intelligence and Soft Computing Research

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Fuzzy logic systems, unlike black-box models, are known as transparent artificial intelligence systems that have explainable rules of reasoning. Type 2 fuzzy systems extend the field of application to tasks that require the introduction of uncertainty in the rules, e.g. for handling corrupted data. Most practical implementations use interval type-2 sets and process interval membership grades. The key role in the design of type-2 interval fuzzy logic systems is played by the type-2 inference defuzzification method. In type-2 systems this generally takes place in two steps: type-reduction first, then standard defuzzification. The only precise type-reduction method is the iterative method known as Karnik-Mendel (KM) algorithm with its enhancement modifications. The known non-iterative methods deliver only an approximation of the boundaries of a type-reduced set and, in special cases, they diminish the profits that result from the use of type-2 fuzzy logic systems. In this paper, we propose a novel type-reduction method based on a smooth approximation of maximum/minimum, and we call this method a smooth type-reduction. Replacing the iterative KM algorithm by the smooth type-reduction, we obtain a structure of an adaptive interval type-2 fuzzy logic which is non-iterative and as close to an approximation of the KM algorithm as we like.

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Section: Discussionmentioning

confidence: 99%

A Novel Approach to Type-Reduction and Design of Interval Type-2 Fuzzy Logic Systems

Starczewski

Przybyszewski

Byrski

et al. 2022

Journal of Artificial Intelligence and Soft Computing Research

Self Cite

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“…where x 1 , x 2 , x 3 , and x 4 denote boundary values of G 1 , G 2 , G 3 , and G 4 , respectively [29].…”

Section: Fault Diagnosis Methods Of Transmission Network Considering Meteorological Factorsmentioning

confidence: 99%

A Fault Diagnosis Method Considering Meteorological Factors for Transmission Networks Based on P Systems

Chen

Wang

Ying

et al. 2021

Entropy

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Bad meteorological conditions may reduce the reliability of power communication equipment, which can increase the distortion possibility of fault information in the communication process, hence raising its uncertainty and incompleteness. To address the issue, this paper proposes a fault diagnosis method for transmission networks considering meteorological factors. Firstly, a spiking neural P system considering a meteorological living environment and its matrix reasoning algorithm are designed. Secondly, based on the topology structure of the target power transmission network and the action logic of its protection devices, a diagnosis model based on the spiking neural P system considering the meteorological living environment is built for each suspicious fault transmission line. Following this, the action messages of protection devices and corresponding temporal order information are used to obtain initial pulse values of input neurons of the diagnosis model, which are then modified with the gray fuzzy theory. Finally, the matrix reasoning algorithm of each model is executed in a parallel manner to obtain diagnosis results. Experiment results achieved out on IEEE 39-bus system show the feasibility and effectiveness of the proposed method.

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“…is the differential translation vector. The output layer is backwardly recursive by using BP network to optimize the weights of the r layer and improve accuracy of the deviation [16]. The details are as follows:…”

Section: Multi-module Retrieval Of Music Resources Based On Feature E...mentioning

confidence: 99%

Massive Music Resources Retrieval Method Based on Ant Colony Algorithm

2024

KSII TIIS

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Music resources are characterized by quantization, diversification and complication. With the rapid increase of the demand for music resources, the storage of music resources is very large. In order to improve the retrieval effect of music resources, a massive music resources retrieval method based on ant colony algorithm is proposed to effectively use music resources. This paper constructs autocorrelation function to extract pitch feature of music resource, classifies the music resource information by calculating feature similarity. Using ant colony algorithm to correlate the feature of music resource, gain the result of correlative, locate the result of detection and get the result of multi-module. Simulation results show that the proposed method has high precision and recall, short retrieval time and can effectively retrieve massive music resources.

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Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems

Cited by 23 publications

References 32 publications

A Novel Approach to Type-Reduction and Design of Interval Type-2 Fuzzy Logic Systems

A Novel Approach to Type-Reduction and Design of Interval Type-2 Fuzzy Logic Systems

A Fault Diagnosis Method Considering Meteorological Factors for Transmission Networks Based on P Systems

Massive Music Resources Retrieval Method Based on Ant Colony Algorithm

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