Self-Tuning of Fuzzy Reasoning by the Steepest Descent Method and Its Application to a Parallel Parking

Hitoshi MIYATA; Makoto OHKI; Masaaki OHKITA

Self-Tuning of Fuzzy Reasoning by the Steepest Descent Method and Its Application to a Parallel Parking

Hitoshi MIYATA, Makoto OHKI, Masaaki OHKITA

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For a fuzzy control of manipulated variable so as to match a required output of a plant, tuning of fuzzy rules are necessary. For its purpose, various methods to tune their rules automatically have been proposed. In these method, some of them necessitate much time for its tuning, and the others are lacking in the generalization capability. In the fuzzy control by the steepest descent method, a use of piecewise linear membership functions (MSFs) has been proposed. In this algorithm, MSFs of the premise for each fuzzy rule are tuned having no relation to the other rules. Besides, only the MSFs corresponding to the given input and output data for the learning can be tuned efficiently. Comparing with the conventional triangular form and the Gaussian distribution of MSFs, an expansion of the expressiveness is indicated. As a result, for constructing the inference rules, the training cycles can be reduced in number and the generalization capability to express the behavior of a plant is expansible. An effectiveness of this algorithm is illustrated with an example of a parallel parking of an autonomous mobile robot.

Publication: IEICE TRANSACTIONS on Information Vol.E79-D No.5 pp.561-569

Publication Date: 1996/05/25

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Type of Manuscript: PAPER

Category: Algorithm and Computational Complexity

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Hitoshi MIYATA, Makoto OHKI, Masaaki OHKITA, "Self-Tuning of Fuzzy Reasoning by the Steepest Descent Method and Its Application to a Parallel Parking" in IEICE TRANSACTIONS on Information, vol. E79-D, no. 5, pp. 561-569, May 1996, doi: .
Abstract: For a fuzzy control of manipulated variable so as to match a required output of a plant, tuning of fuzzy rules are necessary. For its purpose, various methods to tune their rules automatically have been proposed. In these method, some of them necessitate much time for its tuning, and the others are lacking in the generalization capability. In the fuzzy control by the steepest descent method, a use of piecewise linear membership functions (MSFs) has been proposed. In this algorithm, MSFs of the premise for each fuzzy rule are tuned having no relation to the other rules. Besides, only the MSFs corresponding to the given input and output data for the learning can be tuned efficiently. Comparing with the conventional triangular form and the Gaussian distribution of MSFs, an expansion of the expressiveness is indicated. As a result, for constructing the inference rules, the training cycles can be reduced in number and the generalization capability to express the behavior of a plant is expansible. An effectiveness of this algorithm is illustrated with an example of a parallel parking of an autonomous mobile robot.
URL: https://global.ieice.org/en_transactions/information/10.1587/e79-d_5_561/_p

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@ARTICLE{e79-d_5_561,
author={Hitoshi MIYATA, Makoto OHKI, Masaaki OHKITA, },
journal={IEICE TRANSACTIONS on Information},
title={Self-Tuning of Fuzzy Reasoning by the Steepest Descent Method and Its Application to a Parallel Parking},
year={1996},
volume={E79-D},
number={5},
pages={561-569},
abstract={For a fuzzy control of manipulated variable so as to match a required output of a plant, tuning of fuzzy rules are necessary. For its purpose, various methods to tune their rules automatically have been proposed. In these method, some of them necessitate much time for its tuning, and the others are lacking in the generalization capability. In the fuzzy control by the steepest descent method, a use of piecewise linear membership functions (MSFs) has been proposed. In this algorithm, MSFs of the premise for each fuzzy rule are tuned having no relation to the other rules. Besides, only the MSFs corresponding to the given input and output data for the learning can be tuned efficiently. Comparing with the conventional triangular form and the Gaussian distribution of MSFs, an expansion of the expressiveness is indicated. As a result, for constructing the inference rules, the training cycles can be reduced in number and the generalization capability to express the behavior of a plant is expansible. An effectiveness of this algorithm is illustrated with an example of a parallel parking of an autonomous mobile robot.},
keywords={},
doi={},
ISSN={},
month={May},}

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TY - JOUR
TI - Self-Tuning of Fuzzy Reasoning by the Steepest Descent Method and Its Application to a Parallel Parking
T2 - IEICE TRANSACTIONS on Information
SP - 561
EP - 569
AU - Hitoshi MIYATA
AU - Makoto OHKI
AU - Masaaki OHKITA
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E79-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 1996
AB - For a fuzzy control of manipulated variable so as to match a required output of a plant, tuning of fuzzy rules are necessary. For its purpose, various methods to tune their rules automatically have been proposed. In these method, some of them necessitate much time for its tuning, and the others are lacking in the generalization capability. In the fuzzy control by the steepest descent method, a use of piecewise linear membership functions (MSFs) has been proposed. In this algorithm, MSFs of the premise for each fuzzy rule are tuned having no relation to the other rules. Besides, only the MSFs corresponding to the given input and output data for the learning can be tuned efficiently. Comparing with the conventional triangular form and the Gaussian distribution of MSFs, an expansion of the expressiveness is indicated. As a result, for constructing the inference rules, the training cycles can be reduced in number and the generalization capability to express the behavior of a plant is expansible. An effectiveness of this algorithm is illustrated with an example of a parallel parking of an autonomous mobile robot.
ER -