Optimized and improved fuzzy regression model construction method based on nondominated sorting genetic algorithm II (NSGA- II)

A technology of regression model and construction method, applied in the direction of fuzzy logic-based systems, pulse technology, logic circuits, etc., can solve problems such as redundancy of former parts

Inactive Publication Date: 2013-07-10
NANJING UNIV OF SCI & TECH
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Problems solved by technology

[0004] The above technologies have optimized the fuzzy regression model, which has improved the interpre...

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  • Optimized and improved fuzzy regression model construction method based on nondominated sorting genetic algorithm II (NSGA- II)
  • Optimized and improved fuzzy regression model construction method based on nondominated sorting genetic algorithm II (NSGA- II)
  • Optimized and improved fuzzy regression model construction method based on nondominated sorting genetic algorithm II (NSGA- II)

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Embodiment

[0092] In the following embodiments, the number of fuzzy rules, the total number of rule antecedents and the mean square error are selected to evaluate the regression effect of the model.

[0093] Generally speaking, the smaller the mean square error, the higher the accuracy of the fuzzy regression model, the smaller the number of fuzzy rules and the total number of antecedents of the rules, and the better the interpretability of the fuzzy regression model.

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Abstract

The invention discloses an optimized and improved fuzzy regression model construction method based on a nondominated sorting genetic algorithm II (NSGA-II). The optimized and improved fuzzy regression model construction method based on the NAGA-II reduces fuzzy sets, fuzzy rule and redundancy of antecedent of the fuzzy rule, and improves explanatory of a fuzzy regression model. The optimized and improved fuzzy regression model construction method based on the NSGA-II comprises the following steps: firstly an initial fuzzy regression model is constructed by a triangle subordinate function and a WM (WangandMendel) algorithm; and then based on a NSGA-II optimized fuzzy regression model, the redundancy of the fuzzy rule is simultaneously deleted by selecting the fuzzy rule and the antecedent of the fuzzy rule, thereby improving accuracy and explanatory of the fuzzy regression model.

Description

technical field [0001] The present invention relates to the technical field of data mining and artificial intelligence, in particular to an optimized and improved fuzzy regression model construction method based on the second-generation non-dominated sorting genetic algorithm (Non-dominated sorting genetic algorithm II, NSGA-II). Background technique [0002] The knowledge expression form and reasoning mechanism of the fuzzy regression model conform to human thinking habits, and its structure and fuzzy set membership function parameters have obvious physical meaning. People can gain insight into the internal operation mechanism of regression models through easy-to-understand fuzzy rules, that is, interpretability is the most prominent feature of fuzzy regression models. [0003] With the increase of the dimension and complexity of the regression problem, there are mainly the following problems in the construction of the fuzzy regression model using the traditional method, wh...

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Application Information

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IPC IPC(8): G06N7/02
Inventor 邢宗义季海燕刘萍李建伟冒玲丽郭翔
Owner NANJING UNIV OF SCI & TECH
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