A neural network incorporating coupled gradient descent method

被引：0
|
作者：
Isokawa, T [1 ]
Matsui, N [1 ]
Peper, F [1 ]
机构：
[1] Himeji Inst Technol, Dept Comp Engn, Himeji, Hyogo 6712201, Japan
来源：
KNOWLEDGE-BASED INTELLIGENT INFORMATION ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, PTS 1 AND 2 | 2001年 / 69卷
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Back Propagation (BP) algorithm for learning method of layered neural network is widely used in many areas of applications. However, BP has several important drawbacks. One of them is "standstill in learning", and it means that the state of network is subject to getting trapped in one of local minima. In this work, we present the Coupled Gradient Descent Method (CGDM) as an efficient way of avoiding local minima in the state of network.
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页码：162 / 165
页数：4
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