大数据中具有自适应去模糊化的可扩展进化语言模糊系统

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI:10.1109/FUZZ-IEEE.2017.8015753

Antonio A. Márquez, F. A. Márquez, A. Peregrín

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引用次数: 9

摘要

本研究从大数据环境中的模糊回归实例出发，设计可扩展的方法来构建基于语言模糊规则的系统的规则库。我们提出了一种分布式MapReduce模型，该模型基于对经典数据驱动方法的改进，然后采用进化自适应去模糊化来提高最终模糊模型的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A scalable evolutionary linguistic fuzzy system with adaptive defuzzification in big data

This work deals with the design of scalable methodologies to build the Rule Bases of Linguistic Fuzzy Rule Based Systems from examples for Fuzzy Regression in Big Data environments. We propose a distributed MapReduce model based on the use of an adaptation of a classic data driven method followed by an Evolutionary Adaptive Defuzzification to increase the accuracy of the final fuzzy model.

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来源期刊

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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