Multilayer perceptrons and radial basis functions are universal robust approximators

被引：0

作者：

Lo, JTH ^{[1
]}

机构：

[1] Univ Maryland, Dept Math & Stat, Baltimore, MD 21228 USA

来源：

IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE | 1998年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The standard risk-sensitive (or exponential quadratic) functional used for robust control and filtering for linear systems is generalized. It is then shown that under relatively mild conditions, a function can be approximated, to any desired degree of accuracy with respect to these general risk-sensitive functionals, by a multilayer perceptron or a radial basis function network.

引用

页码：1311 / 1314

页数：4

共 50 条

[31] Can neural nets be universal approximators for fuzzy functions?
Buckley, JJ
Hayashi, Y
PROCEEDINGS OF THE SIXTH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS I - III, 1997, : 1101 - 1104
[32] Uncertainty of data, fuzzy membership functions, and multilayer perceptrons
Duch, W
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2005, 16 (01): : 10 - 23
[33] Classification methodologies of multilayer perceptrons with sigmoid activation functions
Gao, DQ
Ji, Y
PATTERN RECOGNITION, 2005, 38 (10) : 1469 - 1482
[34] On variable sizes and sigmoid activation functions of multilayer perceptrons
Gao, DQ
Liu, H
Li, CW
PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4, 2003, : 2017 - 2022
[35] A Robust Meshless Method with QR-Decomposed Radial Basis Functions
Yang, Shunchuan
Chen, Zhizhang
Yu, Yiqiang
Ponomarenko, Sergey
2015 IEEE MTT-S INTERNATIONAL MICROWAVE SYMPOSIUM (IMS), 2015,
[36] Radial Basis Functions
Giesl, Peter
CONSTRUCTION OF GLOBAL LYAPUNOV FUNCTIONS USING RADIAL BASIS FUNCTIONS, 2007, 1904 : 61 - 98
[37] Nonlinear robust control of Cuk converter using radial basis functions
Medagam, Peda
Pourboghrat, Farzad
2006 38TH ANNUAL NORTH AMERICAN POWER SYMPOSIUM, NAPS-2006 PROCEEDINGS, 2006, : 229 - +
[38] Experimental design of supervisory control functions based on multilayer perceptrons
Kukolj, Dragan D.
Berko-Pusic, Miroslava T.
Atlagic, Branislav
Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, 2001, 15 (05): : 425 - 431
[39] Training multilayer perceptrons via minimization of sum of ridge functions
Wu, W
Feng, GR
Li, X
ADVANCES IN COMPUTATIONAL MATHEMATICS, 2002, 17 (04) : 331 - 347
[40] On the transformation mechanisms of multilayer perceptrons with sigmoid activation functions for classifications
Gao, DQ
Zhu, HJ
Nie, GP
PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4, 2003, : 1173 - 1178

← 1 2 3 4 5 →