Two-level learning algorithm for multilayer neural networks

被引：1

作者：

Liu, CS ^{[1
]}

Tseng, CH ^{[1
]}

机构：

[1] Natl Chiao Tung Univ, Dept Mech Engn, Appl Optimum Design Lab, Hsinchu 30050, Taiwan

来源：

TENTH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 1998年

关键词：

D O I：

10.1109/TAI.1998.744795

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A two-level learning algorithm that decomposes multilayer neural networks into a set of sub-networks is presented. A lot of popular optimization methods, such as conjugate-gradient and quasi-Neurton methods, cart be utilized to train these sub-networks. In addition, if the activation functions are hard-limiting functions, the multilayer neural networks can be trained by the perceptron learning rule in this two-level learning algorithm. Two experimental problems are given as examples for this algorithm.

引用

页码：97 / 102

页数：6

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