Monte Carlo Methods in Fuzzy Optimization pp 127-137 | Cite as
Univariate Fuzzy Nonlinear Regression
Introduction
This chapter is concerned with fuzzy nonlinear regression. One of the problems in fuzzy linear regression is to find the “best” values of triangular (trapezoidal) (shaped) fuzzy numbers \(\overline{A}\) and \(\overline{B}\) so that the fuzzy linear function \(\overline{Y}=\overline{A} \; \overline{X} + \overline{B}\) “explains” the fuzzy data \((\overline{X}_l,\overline{Z}_l)\), 1 ≤ l ≤ n. In fuzzy nonlinear regression we are looking for a fuzzy polynomial, or a fuzzy exponential, or a fuzzy logarithmic, ..., function that “explains” the data. In the next section we discuss univariate (one independent variable) fuzzy nonlinear regression. The next chapter continues this discussion where there is more than one independent (explanatory) variable.
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