A Three-Domain Fuzzy Support Vector Regression for Image Denoising and Experimental Studies

被引：33

作者：

Liu, Zhi ^{[1
]}

Xu, Shuqiong ^{[1
,2
]}

Chen, C. L. Philip ^{[4
]}

Zhang, Yun ^{[1
]}

Chen, Xin ^{[3
]}

Wang, Yaonan ^{[5
]}

机构：

[1] Guangdong Univ Technol, Fac Automat, Guangzhou 510006, Guangdong, Peoples R China

[2] Dongguan Polytech, Dept Elect Engn, Dongguan 523808, Peoples R China

[3] Guangdong Univ Technol, Sch Mechatron Engn, Guangzhou 510006, Guangdong, Peoples R China

[4] Univ Macau, Fac Sci & Technol, Taipa, Macau, Peoples R China

[5] Hunan Univ, Dept Elect Engn, Changsha 410082, Hunan, Peoples R China

来源：

IEEE TRANSACTIONS ON CYBERNETICS | 2014年 / 44卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Fuzzy support vector regression (FSVR); three-domain fuzzy kernel function (3DFKF); three-domain fuzzy support vector regression (3DFSVR); uncertain data; MACHINES;

D O I：

10.1109/TSMCC.2013.2258337

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A novel three-domain fuzzy support vector regression (3DFSVR) is proposed, where the three-domain fuzzy kernel function (3DFKF) provides a solution to process uncertainties and input-output data information simultaneously. When compared with traditional two-domain SVR (2DSVR), the major advantage of 3DFSVR is able to use the prior knowledge via the novel fuzzy domain to analyze uncertain data and signals, which will enhance the potentials of 2DSVR. The 3DFKF is presented to integrate the kernel and fuzzy membership functions into a three-domain function. Definition and solution of the fuzzy convex optimization problem are presented to construct the whole theoretical framework. Experiments and simulation results show the effectiveness of 3DFSVR for the uncertain image denoising.

引用

页码：516 / 525

页数：10

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