LEARNING AND GENERALIZATION CHARACTERISTICS OF THE RANDOM VECTOR FUNCTIONAL-LINK NET

被引：838
|
作者：
PAO, YH [1 ]
PARK, GH [1 ]
SOBAJIC, DJ [1 ]
机构：
[1] CASE WESTERN RESERVE UNIV,CLEVELAND,OH 44106
来源：
NEUROCOMPUTING | 1994年 / 6卷 / 02期
关键词：
NEURAL NET; FUNCTIONAL-LINK NET; FUNCTIONAL MAPPING; GENERALIZED DELTA RULE; AUTO-ENHANCEMENT; OVERTRAINING AND GENERALIZATION;
D O I：
10.1016/0925-2312(94)90053-1
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we explore and discuss the learning and generalization characteristics of the random vector version of the Functional-link net and compare these with those attainable with the GDR algorithm. This is done for a well-behaved deterministic function and for real-world data. It seems that 'overtraining' occurs for stochastic mappings. Otherwise there is saturation of training.
引用
收藏
页码：163 / 180
页数：18
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