LEARNING CAPABILITY OF T-MODEL NEURAL NETWORK

被引：0
|
作者：
ISHIZUKA, O
TANG, Z
INOUE, T
MATSUMOTO, H
机构：
来源：
IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES | 1992年 / E75A卷 / 07期
关键词：
NEURAL NETWORK; FULLY INTERCONNECTION; HALF INTERCONNECTION; FEEDFORWARD NETWORK;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
We introduce a novel neural network called the T-Model and investigates the learning ability of the T-Model neural network. A learning algorithm based on the least mean square (LMS) algorithm is used to train the T-Model and produces a very good result for the T-Model network. We present simulation results on several practical problems to illustrate the efficiency of the learning techniques. As a result, the T-Model network learns successfully, but the Hopfield model fails to and the T-Model learns much more effectively and more quickly than a multi-layer network.
引用
收藏
页码：931 / 936
页数：6
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