Application of an Adaptive-Network-Based Fuzzy Inference System for the Optimal Design of a Chinese Braille Display

doi:10.4015/S1016237205000093

機構典藏 > College of Engineering > Graduate Institute & Department of Mechanical and Electro-Mechanical Engineering > Journal Article > Item 987654321/45729

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Title:	Application of an Adaptive-Network-Based Fuzzy Inference System for the Optimal Design of a Chinese Braille Display
Authors:	Yeh, Fung-huei;Tsay, Huoy-shyi;Liang,Shih-hao
Contributors:	淡江大學機械與機電工程學系
Date:	2005-02
Issue Date:	2010-03-26 19:50:12 (UTC+8)
Publisher:	Singapore: World Scientific Publishing Co. Pte. Ltd.
Abstract:	This paper develops a forward-inverse prediction technique of an adaptive-network-based fuzzy inference system (ANFIS) to optimize the design of a relay-actuated Chinese Braille display (CBD) and to reduce the operation temperature. The key element in CBD, electromagnetic actuator assembled by relay, level, and plastic pin, must operate in lower temperature in order to have enough functional force exerted by relay to lift up Braille dots (pin-heads). However, electromagnetic actuators are often destroyed by thermal damage and the thrust force sensed from Braille dots is gradually reduced by the raising of operation temperature. Two design parameters that have the major influence on the thrust force and maximum operation temperature, including supply voltage and coil resistance of relay, were first analyzed by the thermal and electromagnetic numerical forward models to establish the basic databases for ANFIS. Using these databases, the optimal design of CBD can be forward-inversely predicted by ANFIS. As a verification of this forward-inverse mode, the forward predicted maximum operation temperature and the design thrust force inversely predicting the optimal supply voltage by ANFIS were compared with experimental data. The comparison indicates that the maximum operation temperature and the thrust force of both natural and force convection models achieved satisfactory accuracy. When two bell-shape membership functions were adopted, the best accuracy of the forward predicted maximum temperature of ANFIS reached is as high as 99.71% for natural convection model, and the best accuracy of the design thrust force for the inversely predicted supply voltage of ANFIS reached is also as high as 95.50% for force convection model. This investigation proves that the forward-inverse prediction mode developed for ANFIS with thermal and electromagnetic analyses can supply a useful soft computing technique for the optimal design of CBD.
Relation:	Biomedical Engineering: Applications, Basis and Communications 17(1), pp.50-60
DOI:	10.4015/S1016237205000093
Appears in Collections:	[Graduate Institute & Department of Mechanical and Electro-Mechanical Engineering] Journal Article

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