Iterative gradient descent approach to multiple regression with fuzzy data

In this paper, we propose an iterative algorithm for multiple regression with fuzzy independent and dependent variables. While using the standard least squares criterion as a performance index we pose the regression problem as a gradient descent optimisation. Since the differentiation and summation are interchangeable we can calculate the gradient as a sum of separate components thus avoiding undue complication of analytical formulas for multiple regression. We discuss the computational complexity of the proposed algorithm.