Evolutionary learning of nearest-neighbor MLP

被引：34

作者：

Zhao, QF ^{[1
]}

Higuchi, T ^{[1
]}

机构：

[1] TOHOKU UNIV,GRAD SCH INFORMAT SCI,SENDAI,MIYAGI 98077,JAPAN

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 1996年 / 7卷 / 03期

关键词：

D O I：

10.1109/72.501733

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The nearest neighbor multilayer perceptron (NN-MLP) is a single hidden-layer network suitable for pattern recognition. To design an NN-MLP efficiently, this paper proposes a new evolutionary algorithm consisting of four basic operations: recognition, remembrance, reduction, and review. Experimental results show that this algorithm can produce the smallest or nearly smallest networks from random initial ones.

引用

页码：762 / 767

页数：6

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