Iterative gradient descent approach to multiple regression with fuzzy data

A. Bargiela; T. Nakashima; W. Pedrycz; IEEE

doi:10.1109/NAFIPS.2005.1548552

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Iterative gradient descent approach to multiple regression with fuzzy data

A. Bargiela, T. Nakashima, W. Pedrycz and IEEE

NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society, Vol.2005, pp.304-309

2005

DOI: https://doi.org/10.1109/NAFIPS.2005.1548552

Abstract

Educational institutions

fuzzy data

Fuzzy sets

gradient descent

Informatics

Iterative algorithms

Iterative methods

Least squares methods

Linear regression

multiple regression

Performance analysis

Regression analysis

Vectors

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.

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Details

Title: Iterative gradient descent approach to multiple regression with fuzzy data
Creators - without role: A. Bargiela - Nottingham Trent University
T. Nakashima
W. Pedrycz
IEEE
Publication Details: NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society, Vol.2005, pp.304-309
Publisher: IEEE
Identifiers: 9937818908331
Academic Unit: King Abdulaziz University
Language: English
Resource Type: Conference proceeding