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Explanatable mixed type fuzzy system optimization method based on multi-objective ant colony algorithm

A mixed type, fuzzy system technology, applied in fuzzy logic-based systems, calculation models, calculations, etc., can solve problems such as large amount of calculation and complex similarity measurement of fuzzy sets

Pending Publication Date: 2021-05-07
SICHUAN UNIV
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Problems solved by technology

However, the fuzzy set similarity measures mentioned above are relatively complex and computationally intensive.

Method used

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  • Explanatable mixed type fuzzy system optimization method based on multi-objective ant colony algorithm
  • Explanatable mixed type fuzzy system optimization method based on multi-objective ant colony algorithm
  • Explanatable mixed type fuzzy system optimization method based on multi-objective ant colony algorithm

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Embodiment Construction

[0095]DETAILED DESCRIPTION OF THE INVENTION The present invention will be described below to understand the present invention, but it should be understood, and the present invention is not limited to the scope of the specific embodiments, and in terms of ordinary skill in the art, as long as various changes Within the spirit and scope of the invention appended claims, it is apparent from the spirit and scope of the appended claims, and all inventions of the inventive concepts are all protected.

[0096]Such asfigure 1 As shown, the embodiment of the present invention provides a method of explaining a multi-target ant colony algorithm, a method of optimizing a hybrid type, including steps S1 to S3:

[0097]S1, construct an explanatory mixed type fuzzy system;

[0098]In the present embodiment, the interpretable mixed type fuzzy system constructed in the present invention includes a blur, a throttle, a droper, and ambiguator. Its structurefigure 2 Indicated.

[0099]The ambiguator maps a clear va...

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Abstract

The invention discloses an interpretable mixed type fuzzy system optimization method based on the multi-objective ant colony algorithm. The method comprises the following steps: constructing an interpretable mixed type fuzzy system; constructing an initial reference rule vector by adopting a fuzzy set online clustering updating algorithm; and optimizing system parameters of the mixed type fuzzy system by adopting an improved multi-target leading edge oriented continuous ant colony optimization algorithm. According to the method, the interpretable mixed type fuzzy system is constructed according to the constraint of the uncertain coverage domain of the fuzzy set, so that the generation of a redundant interval type-2 fuzzy set is effectively avoided; an initial reference rule vector is constructed by adopting a fuzzy set online clustering updating algorithm, so that the calculation is relatively simple, and the characteristics of traditional similarity measurement based on a set theory are reserved; and finally, an improved multi-target leading edge oriented continuous ant colony optimization algorithm is adopted to optimize the control performance and the interpretability at the same time, and better balance between the interpretability and the control performance of the fuzzy controller is realized.

Description

Technical field[0001]The present invention relates to the field of mixing type fuzzy system design technology, and more particularly to an explanatory mixed type fuzzy system optimization method based on multi-target ant colony algorithm.Background technique[0002]In recent decades, fuzzy logic systems have been widely used in various fields. This is benefited from the fuzzy system to effectively utilize expert experience and can be used as the characteristics of universal approximation. However, prior knowledge of the controlled system is not necessarily acquired. Data-driven self-organized fuzzy systems have therefore received more and more attention. It is often an important role in learning the systematic lack of interpretability, and interpretive has an important role in assistance decisions. The current self-organizing fuzzy system is often carried out on the framework of a blurred system. In many application scenarios, the performance of the interval secondary fuzzy system is ...

Claims

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Application Information

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IPC IPC(8): G06N7/02G06N3/00
CPCG06N3/006G06N7/023
Inventor 赵涛陈成森佃松宜
Owner SICHUAN UNIV
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