Abstract
The common problem with the hierarchical tuning methods is the lack of conditions for modification of the primary rules. The incremental approach accelerates the generation of candidate rules, but complicates the selection of the primary and modified rules. In the paper, the approach that combines semantic training, granular partition and solution of fuzzy relational equations for constructing accurate and interpretable rules is developed. The composite fuzzy model of direct logic inference based on the primary rules with granular parameters is proposed. The method of hierarchical tuning with the linguistic modification based on solving fuzzy relational equations is developed, which allows reducing the training time. It is shown that the weights of the primary rules, which are subject to modification, as well as the hedging threshold of the primary terms, are solutions of the primary system of fuzzy logic equations with the hierarchical max-min/min-max composition, which solves the problem of the hierarchical selection of the primary and modified rules for the given output classes. The genetic-neural approach was used for tuning the primary rules and solving the system of equations, as well as tuning the composite rules. The effectiveness of the approach is illustrated by the example of tuning and interpreting the solutions to the technological process quality control problem for the specified productivity classes. The primary model with granular parameters allows reducing the tuning error by 25 % compared to the primary relational model. The solution of the hierarchical selection problem allows reducing the tuning time by half.
Highlights
Hierarchical tuning is used to ensure the accuracy and interpretability of fuzzy models
The composite fuzzy model of direct logic inference based on the primary rules with granular parameters is proposed
It is shown that the weights of the primary rules, which are subject to modification, as well as the hedging threshold of the primary terms, are solutions of the primary system of fuzzy logic equations with the hierarchical max-min/minmax composition, which solves the problem of the hierarchical selection of the primary and modified rules for the given output classes
Summary
Hierarchical tuning is used to ensure the accuracy and interpretability of fuzzy models. The model of the lowest level is constructed using linguistic modifiers (strong, weak) that reflect the semantic intensity of the primary terms. The modified candidate rules are generated for each primary rule, and the knowledge base is subject to further selection and reduction [1]. The linguistic modification is carried out by the concentration of the primary term with the subsequent shift. The solution to the problem of rule selection may be the use of fuzzy relational equations [3], the solutions of which represent the linguistic modification of the primary terms. It is important to develop a composite approach combining the benefits of semantic training, granular partition and fuzzy relational equations in simplification of the process of hierarchical tuning of fuzzy classification knowledge bases
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Eastern-European Journal of Enterprise Technologies
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
