Support Vector Machine Regression for a Gaussian Fuzzy Model

• 발행기관: 한국자료분석학회
• 저자명: Changha Hwang
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.6 No.2, 431~439쪽, 전체 9쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2004.04.30

영문 초록

Support vector machine(SVM) has been very successful in pattern recognition and function estimation problems for crisp data. In this paper, we propose SVM approach to evaluating fuzzy regression model with multiple crisp inputs and a Gaussian fuzzy output. The proposed algorithm here is model-free method in the sense that we do not need assume the underlying model function. This algorithm is robust for the estimation of fuzzy linear and nonlinear regression models, especially when outliers are present. Numerical examples are given to detail the effectiveness of this approach.

목차

1. Introduction

2. Distance of Gaussian Fuzzy Numbers

3. SVM Regression of Fuzzy Model

4. Numerical Examples

References

키워드

Fuzzy least squares regression support vector machine