Matching decision model for self- adaptability of knowledge manufacturing system

Abstract

Knowledge manufacturing system has the ability of modifying dynamically manufacturing mode rapidly when production environment factors change. It is essential to evaluate the matching degree of established manufacturing mode and changed production environment factors. In this paper, a matching decision model for self-adaptability of knowledge manufacturing system based on the fuzzy neural network is proposed. The changed production environment factors are regarded as linguistic variable inputs. A modified momentum factor B-P algorithm consisting of information feed-forward process and the error back-propagation process is used. The proposed FNN model is employed to evaluate the matching degree of a car-lamp production manufacturing mode to variable environment units. Matching result indicates adaptive degree of manufacturing system. Experiment result demonstrates the method is effective.