MODIFIED HEBBIAN LEARNING FOR CURVE AND SURFACE FITTING

被引：165

作者：

XU, L

OJA, E

SUEN, CY

机构：

[1] CONCORDIA UNIV,CTR PATTERN RECOGNIT & MACHINE INTELLIGENCE,MONTREAL H3G 1M8,QUEBEC,CANADA

[2] LAPPEENRANTA UNIV TECHNOL,DEPT INFORMAT TECHNOL,SF-53851 LAPPEENRANTA 85,FINLAND

来源：

NEURAL NETWORKS | 1992年 / 5卷 / 03期

关键词：

HEBBIAN LEARNING; PRINCIPAL COMPONENT ANALYSIS; MINOR COMPONENTS; CURVE AND SURFACE FITTING; TOTAL LEAST SQUARE METHOD; STOCHASTIC APPROXIMATION;

D O I：

10.1016/0893-6080(92)90006-5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A linear neural unit with a modified anti-Hebbian learning rule is shown to be able to optimally fit curves, surfaces, and hypersurfaces by adaptively extracting the minor component (i.e., the counterpart of principal component) of the input data set. The learning rule is analyzed mathematically. The results of computer simulations are given to illustrate that this neural fitting method considerably out perform the commonly used least square fitting method in resisting both normal noise and outlier

引用

页码：441 / 457

页数：17

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