Sugeno型系统的优化模糊推理

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS Pub Date : 2022-12-06 DOI:10.3103/S0005105522050028

M. S. Golosovskiy, A. V. Bogomolov, M. E. Balandov

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引用次数: 2

摘要

本文提出了一种优化算法，允许在受RAM和CPU功率限制的计算系统中使用具有大量推理规则（约1000万）的模糊推理系统。优化是通过重新分配输入变量的隶属函数和推理规则的动态形成来实现的，这使得模糊系统只能存储推理规则的结论，而不需要在模糊推理过程中对规则进行完全搜索。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Optimized Fuzzy Inference for Sugeno-Type Systems

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Optimized Fuzzy Inference for Sugeno-Type Systems

The article proposes an optimized algorithm that allows the use of a fuzzy inference system with a large number of inference rules (about 10 million) in computing systems limited by RAM and CPU power. Optimization is achieved by redistributing the membership functions of input variables and the dynamic formation of inference rules, which allows fuzzy system to store only inference rules conclusions, without using a complete search of the rules in the process of fuzzy inference.

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来源期刊

AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS COMPUTER SCIENCE, INFORMATION SYSTEMS-

自引率

40.00%

发文量

期刊介绍： Automatic Documentation and Mathematical Linguistics is an international peer reviewed journal that covers all aspects of automation of information processes and systems, as well as algorithms and methods for automatic language analysis. Emphasis is on the practical applications of new technologies and techniques for information analysis and processing.