Approximations and adaptability of neural networks

被引：0

作者：

Shi, K ^{[1
]}

Fei, SH ^{[1
]}

Lin, C ^{[1
]}

机构：

[1] Harvey Mudd Coll, Claremont, CA 91711 USA

来源：

2005 IEEE International Conference on Granular Computing, Vols 1 and 2 | 2005年

关键词：

adaptability; approximations; learning; neural network;

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：

Given a neural network that approximates a given function which describes some physical phenomenon. If the physical constraints and hence the given function change, neural networks must adapt to the physical world by changing their architecture. The new architecture may have more neurons, so the adaptable neural networks need this learning abilities that are not in traditional design.

引用

页码：253 / 255

页数：3

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