A Rule-Embedded Neural-Network and Its Effectiveness in Pattern Recognition with <cd02d37.gif>-Posed Conditions

Mina MARUYAMA; Nobuo TSUDA; Kiyoshi NAKABAYASHI

A Rule-Embedded Neural-Network and Its Effectiveness in Pattern Recognition with -Posed Conditions

Mina MARUYAMA, Nobuo TSUDA, Kiyoshi NAKABAYASHI

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This paper describes an advanced rule-embedded neural network (RENN⁺) that has an extended framework for achieving a very tight integration of learning-based neural networks and rule-bases of existing if-then rules. The RENN⁺ is effective in pattern recognition with ill-posed conditions. It is basically composed of several component RENNs and an output RENN, which are three-layer back-propagation (BP) networks except for the input layer. Each RENN can be pre-organized by embedding the if-then rules through translation of the rules into logic functions in a disjunctive normal form, and can be trainded to acquire adaptive rules as required. A weight-modification-reduced learning algorithm (WMR) capable of standard regularization is used for the post-training to suppress excessive modification of the weights for the embedded rules. To estimate the effectiveness of the proposed RENN⁺, it was used for pattern recognition in a radar system for detection of buried pipes. This trial showed that a RENN⁺ with two component RENNs had good recognition capability, whereas a conventional BP network was ineffective.

Publication: IEICE TRANSACTIONS on Information Vol.E78-D No.2 pp.152-162

Publication Date: 1995/02/25

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Type of Manuscript: PAPER

Category: Bio-Cybernetics and Neurocomputing

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Mina MARUYAMA, Nobuo TSUDA, Kiyoshi NAKABAYASHI, "A Rule-Embedded Neural-Network and Its Effectiveness in Pattern Recognition with -Posed Conditions" in IEICE TRANSACTIONS on Information, vol. E78-D, no. 2, pp. 152-162, February 1995, doi: .
Abstract: This paper describes an advanced rule-embedded neural network (RENN⁺) that has an extended framework for achieving a very tight integration of learning-based neural networks and rule-bases of existing if-then rules. The RENN⁺ is effective in pattern recognition with ill-posed conditions. It is basically composed of several component RENNs and an output RENN, which are three-layer back-propagation (BP) networks except for the input layer. Each RENN can be pre-organized by embedding the if-then rules through translation of the rules into logic functions in a disjunctive normal form, and can be trainded to acquire adaptive rules as required. A weight-modification-reduced learning algorithm (WMR) capable of standard regularization is used for the post-training to suppress excessive modification of the weights for the embedded rules. To estimate the effectiveness of the proposed RENN⁺, it was used for pattern recognition in a radar system for detection of buried pipes. This trial showed that a RENN⁺ with two component RENNs had good recognition capability, whereas a conventional BP network was ineffective.
URL: https://global.ieice.org/en_transactions/information/10.1587/e78-d_2_152/_p

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@ARTICLE{e78-d_2_152,
author={Mina MARUYAMA, Nobuo TSUDA, Kiyoshi NAKABAYASHI, },
journal={IEICE TRANSACTIONS on Information},
title={A Rule-Embedded Neural-Network and Its Effectiveness in Pattern Recognition with -Posed Conditions},
year={1995},
volume={E78-D},
number={2},
pages={152-162},
abstract={This paper describes an advanced rule-embedded neural network (RENN⁺) that has an extended framework for achieving a very tight integration of learning-based neural networks and rule-bases of existing if-then rules. The RENN⁺ is effective in pattern recognition with ill-posed conditions. It is basically composed of several component RENNs and an output RENN, which are three-layer back-propagation (BP) networks except for the input layer. Each RENN can be pre-organized by embedding the if-then rules through translation of the rules into logic functions in a disjunctive normal form, and can be trainded to acquire adaptive rules as required. A weight-modification-reduced learning algorithm (WMR) capable of standard regularization is used for the post-training to suppress excessive modification of the weights for the embedded rules. To estimate the effectiveness of the proposed RENN⁺, it was used for pattern recognition in a radar system for detection of buried pipes. This trial showed that a RENN⁺ with two component RENNs had good recognition capability, whereas a conventional BP network was ineffective.},
keywords={},
doi={},
ISSN={},
month={February},}

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TY - JOUR
TI - A Rule-Embedded Neural-Network and Its Effectiveness in Pattern Recognition with -Posed Conditions
T2 - IEICE TRANSACTIONS on Information
SP - 152
EP - 162
AU - Mina MARUYAMA
AU - Nobuo TSUDA
AU - Kiyoshi NAKABAYASHI
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E78-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 1995
AB - This paper describes an advanced rule-embedded neural network (RENN⁺) that has an extended framework for achieving a very tight integration of learning-based neural networks and rule-bases of existing if-then rules. The RENN⁺ is effective in pattern recognition with ill-posed conditions. It is basically composed of several component RENNs and an output RENN, which are three-layer back-propagation (BP) networks except for the input layer. Each RENN can be pre-organized by embedding the if-then rules through translation of the rules into logic functions in a disjunctive normal form, and can be trainded to acquire adaptive rules as required. A weight-modification-reduced learning algorithm (WMR) capable of standard regularization is used for the post-training to suppress excessive modification of the weights for the embedded rules. To estimate the effectiveness of the proposed RENN⁺, it was used for pattern recognition in a radar system for detection of buried pipes. This trial showed that a RENN⁺ with two component RENNs had good recognition capability, whereas a conventional BP network was ineffective.
ER -