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

概要

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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 trained 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. multi-layered neural network, rule embedding, pattern classification, weight-modification-reduced learning standard regularization
社団法人電子情報通信学会の論文
1995-02-25