The Hybrid Fuzzy Least-Squares Regression Approach to Modeling Manufacturing Processes

Authors:
C. K. Kwong;Y. Chen;K. Y. Chan;H. Wong
Affiliations:
Dept. of Ind. & Syst. Eng., Hong Kong Polytech Univ., Hong Kong;-;-;-
Venue:
IEEE Transactions on Fuzzy Systems
Year:
2008

Citing 0
Cited 6

Fuzzy regression models using the least-squares method based on the concept of distance

IEEE Transactions on Fuzzy Systems
A hierarchical Mamdani-type fuzzy modelling approach with new training data selection and multi-objective optimisation mechanisms: A special application for the prediction of mechanical properties of alloy steels

Applied Soft Computing
Determining the final priority ratings of customer requirements in product planning by MDBM and BSC

Expert Systems with Applications: An International Journal
Sensor deployment for fault diagnosis using a new discrete optimization algorithm

Applied Soft Computing
A new optimization algorithm for single hidden layer feedforward neural networks11The third author would like to thank the support of RGC Grant PolyU. (5365/09E), NSFC Grant (10901170) and the Research Committee of the Hong Kong Polytechnic University.

Applied Soft Computing
Applying fuzzy multi-objective linear programming to project management decisions with the interactive two-phase method

Computers and Industrial Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Uncertainty in manufacturing processes is caused both by randomness, as in material properties, and by fuzziness, as in the inexact knowledge. Previous research has seldom considered these two types of uncertainty when modeling manufacturing processes. In this paper, a hybrid fuzzy least-squares regression (HFLSR) approach to modeling manufacturing processes, which does take into consideration these two types of uncertainty, is proposed and described, and a new form of weighted fuzzy arithmetic is introduced to develop the hybrid fuzzy least-squares regression method. The proposed HFLSR approach not only features the capability of dealing with the two types of uncertainty, but also addresses the consideration of replication of responses in experiments. To investigate the effectiveness of the proposed approach to process modeling, it was applied to the modeling solder paste dispensing process. Modeling results were compared with those based on statistical regression and fuzzy linear regression. It was found that the accuracy of prediction based on the HFLSR is slightly better than that based on statistical regression and much better than that based on the Peters fuzzy regression.