Fuzzy regression analysis is an important regression analysis method to predict\nuncertain information in the real world. In this paper, the input data are\ncrisp with randomness; the output data are trapezoid fuzzy number, and\nthree different risk preferences and chaos optimization algorithm are introduced\nto establish fuzzy regression model. On the basis of the principle of the\nminimum total spread between the observed and the estimated values,\nrisk-neutral, risk-averse, and risk-seeking fuzzy regression model are developed\nto obtain the parameters of fuzzy linear regression model. Chaos optimization\nalgorithm is used to determine the digital characteristic of random\nvariables. The mean absolute percentage error and variance of errors are\nadopted to compare the modeling results. A stock rating case is used to evaluate\nthe fuzzy regression models. The comparisons with five existing methods\nshow that our proposed method has satisfactory performance.
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