Performance Optimization of the Fuzzy Rule Interpolation Method “FIVE”

Author:

Vincze Dávid, ,Kovács Szilveszter

Abstract

Fuzzy Rule Interpolation (FRI) methods are efficient structures for knowledge-representation with relatively few rules. In spite of their good knowledge representation efficiency, their high computational demand makes the FRI methods hardly suitable for embedded real-time applications, for which short reasoning time has a high importance. On the other hand, the fact that currently available devices have increased computational power gives the FRI methods an opportunity to appear in real-time embedded applications. Therefore, the need for a low-computation and lowresource-demand FRI method is emerging. The goal of this paper is to introduce some implementation details of such an FRI method, together with its brief time and space complexity analysis. The paper also gives some hints for further performance optimization possibilities.

Publisher

Fuji Technology Press Ltd.

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Autonomous robot control using hardware accelerated implementation of fuzzy interpolation;2023 24th International Carpathian Control Conference (ICCC);2023-06-12

2. Benchmark example for the Heuristically accelerated FRIQ-learning;2023 24th International Carpathian Control Conference (ICCC);2023-06-12

3. A New Approach for Transformation-Based Fuzzy Rule Interpolation;IEEE Transactions on Fuzzy Systems;2020-12

4. Noise Reduction with Fuzzy Inference Based on Generalized Mean and Singleton Input–Output Rules: Toward Fuzzy Rule Learning in a Unified Inference Platform;Journal of Advanced Computational Intelligence and Intelligent Informatics;2019-11-20

5. Noise Reduction with Inference Based on Fuzzy Rule Interpolation at an Infinite Number of Activating Points: Toward Fuzzy Rule Learning in a Unified Inference Platform;Journal of Advanced Computational Intelligence and Intelligent Informatics;2018-10-20

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