Paper Title:
Classification Decision of Mechanical Drive Types Using LVQ Neural Classifier in Conceptual Design
  Abstract

In conceptual design of mechanical system, selecting an appropriate mechanical drive type that meets design requirements is a critical problem. To tackle it, a novel classification decision approach to drive type selection is presented, which employs LVQ neural network as classifier to recognize a satisfactory drive from a range of drive types. This recognition process includes five stages: (1) capturing linguistic expression values of character factor for typical drives; (2) digitizing linguistic expression values and extracting features of drives; (3) constructing the structure of a LVQ neural network; (4) training this network with the feature data of character factor set; (5) identifying an appropriate drive type according to the input data using the trained neural network. This proposed approach capitalizes on the benefits of LVQ neural network to acquire domain knowledge and implement classification and decision. Hence at some extent the process of conceptual design can be simplified and the rationality of the decision can be greatly improved.

  Info
Periodical
Chapter
Chapter 3: Engineering Design Theory and Methodology
Edited by
Di Zheng, Yiqiang Wang, Yi-Min Deng, Aibing Yu and Weihua Li
Pages
614-617
DOI
10.4028/www.scientific.net/AMM.101-102.614
Citation
R. F. Bo, "Classification Decision of Mechanical Drive Types Using LVQ Neural Classifier in Conceptual Design", Applied Mechanics and Materials, Vols. 101-102, pp. 614-617, 2012
Online since
September 2011
Authors
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Price
$32.00
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