A Continuous-time Recurrent Neural Network for Real-time Support Vector Regression

被引：0

作者：

Liu, Qingshan ^{[1
]}

Zhao, Yan ^{[2
]}

机构：

[1] Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China

[2] Wannan Med Coll, Dept Basic Courses, Wuhu 241000, Anhui, Peoples R China

来源：

PROCEEDINGS OF THE 2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN CONTROL AND AUTOMATION (CICA) | 2013年

基金：

中国国家自然科学基金;

关键词：

VARIATIONAL-INEQUALITIES; OPTIMIZATION PROBLEMS; ACTIVATION FUNCTION; BOUND CONSTRAINTS; DESIGN;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a continuous-time recurrent neural network described by differential equations for real-time support vector regression (SVR). The SVR is first formulated as a convex quadratic programming problem, and then a continuous-time recurrent neural network with one-layer structure is designed for training the support vector machine. Furthermore, simulation results on an illustrative example are given to demonstrate the effectiveness and performance of the proposed neural network.

引用

页码：189 / 193

页数：5

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