Fuzzy regression model with fuzzy input and output data for manpower forecasting

✍ Scribed by Hong Tau Lee; Sheu Hua Chen

• Publisher: Elsevier Science
• Year: 2001
• Tongue: English
• Weight: 113 KB
• Volume: 119
• Category: Article
• ISSN: 0165-0114
• DOI: 10.1016/s0165-0114(98)00382-0

✦ Synopsis

In modeling a fuzzy system with fuzzy linear functions, the vagueness of the fuzzy output data may be caused by both the indeÿniteness of model parameters and the vagueness of the input data. This situation occurs as the input data are envisaged as facts or events of an observation which are uncontrollable or unin uenced by the observer rather than as the controllable levels of factors in an experiment. In this research, we concentrate on such a situation and refer to it as a generalized fuzzy linear function. Using this generalized fuzzy linear function, a generalized fuzzy regression model is formulated. A nonlinear programming model is proposed to identify the fuzzy parameters and their vagueness for the generalized regression model. A manpower forecasting problem is used to demonstrate the use of the proposed model.