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.