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A genetic algorithm for rule extraction in fuzzy adaptive learning control networks

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Abstract

This paper presents a novel approach, dubbed Falcon-GA, for rule extraction in a Fuzzy Adaptive Learning Control Network (FALCON) using a Genetic Algorithm (GA). The FALCON-GA combines multiple techniques to establish the relationships and connections among fuzzy rules, including the use of a GA for rule extraction and a Gradient-based method for fine-tuning the membership function parameters. The learning algorithm of FALCON-GA incorporates three key components: the ART (Adaptive Resonance Theory) clustering algorithm for initial membership function identification, the Genetic Algorithm for rule extraction, and the Gradient method for adjusting membership function parameters. Moreover, FALCON-GA offers flexibility by allowing the incorporation of different rule types within the FALCON architecture, making it flexible and expansible. The proposed model has been evaluated in various forecasting problems reported in the literature and compared to alternative models. Computational experiments demonstrate the effectiveness of FALCON-GA in forecasting tasks and reveal significant performance improvements compared to the original FALCON. These results indicate that Genetic Algorithms efficiently extract rules for Fuzzy Adaptive Learning Control Networks.

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Acknowledgements

The authors acknowledge CAPES, Brazilian Ministry of Education, code 001.

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Authors and Affiliations

  1. Graduate Program in Mathematical and Computational Modeling, Federal Center of Technological Education of Minas Gerais, Av. Amazonas, 7675, Belo Horizonte, MG, 30510-000, Brazil

    Glender Brás, Alisson Marques Silva & Elizabeth F. Wanner

  2. College of Engineering and Physical Sciences, Aston University, Aston Triangle, Birmingham, UK

    Elizabeth F. Wanner

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  1. Glender Brás
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Brás, G., Silva, A.M. & Wanner, E.F. A genetic algorithm for rule extraction in fuzzy adaptive learning control networks. Genet Program Evolvable Mach 25, 11 (2024). https://doi.org/10.1007/s10710-024-09486-2

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