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Ten years of genetic fuzzy systems: current framework and new trends

机译：遗传模糊系统十年：当前框架和新趋势

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摘要

Fuzzy systems have demonstrated their ability to solve different kinds of problems in various application domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridise fuzzy systems with learning and adaptation methods have been made in the realm of soft computing. Neural fuzzy systems and genetic fuzzy systems hybridise the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms. The objective of this paper is to provide an account of genetic fuzzy systems, with special attention to genetic fuzzy rule-based systems. After a brief introduction to models and applications of genetic fuzzy systems, the field is overviewed, new trends are identified, a critical evaluation of genetic fuzzy systems for fuzzy knowledge extraction is elaborated, and open questions that remain to be addressed in the future are raised. The paper also includes some of the key references required to quickly access implementation details of genetic fuzzy systems.
机译：模糊系统已经证明了其解决各种应用领域中各种问题的能力。当前，越来越有兴趣增加具有学习和适应能力的模糊系统。在软计算领域，已经提出了两种最成功的方法将模糊系统与学习和自适应方法进行混合。神经模糊系统和遗传模糊系统将模糊系统的近似推理方法与神经网络和进化算法的学习能力相结合。本文的目的是提供遗传模糊系统的说明，特别注意基于遗传模糊规则的系统。在简要介绍了遗传模糊系统的模型和应用之后，对该领域进行了概述，确定了新的趋势，阐述了对遗传模糊系统进行模糊知识提取的关键评估，并提出了未来仍有待解决的未解决问题。本文还包括一些快速访问遗传模糊系统实现细节所需的关键参考。

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