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
收藏
页码：97 / 102
页数：6
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