Abstract
Since interval output data can be regarded as distributions of possibility, interval regression analysis is proposed by possibilistic interval systems. Interval data are given by fuzzy observa tion or expert knowledge and it becomes recently important to deal with interval data implying our partial ignorance on the phenomenon. In our formulations, possibility and necessity measures, which are dual each other, are used to construct several formulations of interval regression models, depending on different situations under consideration. Our approach for obtaining a possibilistic interval model which fits to given interval output data can be reduced to LP problems. Thus, merits of our approach are to be able to obtain interval parameters by LP methods and to add expert knowledge on parameters in interval models to constraint conditions in LP problems. Some examples are depicted to illustrate these new techniques.
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