Fuzzy Inference Systems Composed of Double-Input Rule Modules for Obstacle Avoidance Problems
The purpose of self-tuning algorithm for fuzzy inference system is to construct automatically fuzzy inference rules from learning data based on the steepest descend method. Obvious drawbacks of the method are its large computational complexity and getting stuck in a shallow local minimum. Further, i...
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Published in | IAENG international journal of computer science Vol. 41; no. 4; pp. 222 - 230 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
01.12.2014
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Subjects | |
Online Access | Get full text |
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Summary: | The purpose of self-tuning algorithm for fuzzy inference system is to construct automatically fuzzy inference rules from learning data based on the steepest descend method. Obvious drawbacks of the method are its large computational complexity and getting stuck in a shallow local minimum. Further, it is difficult to apply for the conventional method to the problem with a large number of variables. In order to overcome them, the SIRMs (Single-Input Rule Modules) and DIRMs (Double-Input Rule Modules) models have been proposed. In some numerical simulations, it is shown that there exists the difference of the ability between DIRMs and SIRMs models. In this paper, we will apply DIRMs and SIRMs models to the control problem of obstacle avoidance. As a result, it is shown that DIRMs model is more effective than SIRMs model in this problem. Further, we propose a learning method to reduce the number of modules of DIRMs model and show the effectiveness in numerical simulations. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1819-656X 1819-9224 |