Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
474930 | Computers & Operations Research | 2006 | 13 Pages |
Abstract
Provided with plenty of data (experience), data mining techniques are widely used to extract suitable management skills from the data. Nevertheless, in the early stages of a manufacturing system, only rare data can be obtained, and built scheduling knowledge is usually fragile. Using small data sets, this research's purpose is improving the accuracy of machine learning for flexible manufacturing system (FMS) scheduling. The study develops a data trend estimation technique and combines it with mega-fuzzification and adaptive-network-based fuzzy inference systems (ANFIS). The results of the simulated FMS scheduling problem indicate that learning accuracy can be significantly improved using the proposed method involving a very small data set.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Der-Chiang Li, Chih-Sen Wu, Tung-I Tsai, Fengming M. Chang,