Best approximation to C2 functions and its error bounds using regular-center gaussian networks

Binfan Liu; Jennie Si

Best approximation to C² functions and its error bounds using regular-center gaussian networks

Binfan Liu
, Jennie Si

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Gaussian neural networks are considered to approximate any C² function with support on the unit hypercube I_m = [0,1]^m in the sense of best approximation. An upper bound (O(N^-2)) of the approximation error is obtained in the present paper for a Gaussian network having N^m hidden neurons with centers defined on a regular mesh in I_m.

Original language	English (US)
Title of host publication	IEEE International Conference on Neural Networks - Conference Proceedings
Place of Publication	Piscataway, NJ, United States
Publisher	IEEE
Pages	2400-2406
Number of pages	7
Volume	4
State	Published - 1994
Event	Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA Duration: Jun 27 1994 → Jun 29 1994

Conference

Conference	Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
City	Orlando, FL, USA
Period	6/27/94 → 6/29/94

ASJC Scopus subject areas

Software

Cite this

Liu, B & Si, J 1994, Best approximation to C² functions and its error bounds using regular-center gaussian networks. in IEEE International Conference on Neural Networks - Conference Proceedings. vol. 4, IEEE, Piscataway, NJ, United States, pp. 2400-2406, Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7), Orlando, FL, USA, 6/27/94.

@inproceedings{b24f614c62894e50a66c057419e681fa,

title = "Best approximation to C2 functions and its error bounds using regular-center gaussian networks",

abstract = "Gaussian neural networks are considered to approximate any C2 function with support on the unit hypercube Im = [0,1]m in the sense of best approximation. An upper bound (O(N-2)) of the approximation error is obtained in the present paper for a Gaussian network having Nm hidden neurons with centers defined on a regular mesh in Im.",

author = "Binfan Liu and Jennie Si",

year = "1994",

language = "English (US)",

volume = "4",

pages = "2400--2406",

booktitle = "IEEE International Conference on Neural Networks - Conference Proceedings",

publisher = "IEEE",

note = "Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) ; Conference date: 27-06-1994 Through 29-06-1994",

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TY - GEN

T1 - Best approximation to C2 functions and its error bounds using regular-center gaussian networks

AU - Liu, Binfan

AU - Si, Jennie

PY - 1994

Y1 - 1994

N2 - Gaussian neural networks are considered to approximate any C2 function with support on the unit hypercube Im = [0,1]m in the sense of best approximation. An upper bound (O(N-2)) of the approximation error is obtained in the present paper for a Gaussian network having Nm hidden neurons with centers defined on a regular mesh in Im.

AB - Gaussian neural networks are considered to approximate any C2 function with support on the unit hypercube Im = [0,1]m in the sense of best approximation. An upper bound (O(N-2)) of the approximation error is obtained in the present paper for a Gaussian network having Nm hidden neurons with centers defined on a regular mesh in Im.

UR - https://www.scopus.com/pages/publications/0028748447

UR - https://www.scopus.com/pages/publications/0028748447#tab=citedBy

M3 - Conference contribution

VL - 4

SP - 2400

EP - 2406

BT - IEEE International Conference on Neural Networks - Conference Proceedings

PB - IEEE

CY - Piscataway, NJ, United States

T2 - Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)

Y2 - 27 June 1994 through 29 June 1994

ER -

Best approximation to C² functions and its error bounds using regular-center gaussian networks

Abstract

Conference

ASJC Scopus subject areas

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