A parallel variable memory BFGS training algorithm

被引：0
作者：
Mc Loone, S [1 ]
机构：
[1] Queens Univ Belfast, Sch Elect & Elect Engn, Intelligent Syst & Control Res Grp, Belfast BT9 5AH, Antrim, North Ireland
来源：
ALGORITHMS AND ARCHITECTURES FOR REAL-TIME CONTROL 2000 | 2000年
关键词：
neural networks; parallel algorithms; training; second-order;
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：
This paper considers the parallel implementation of a novel variable memory quasi-newton neural network training algorithm recently developed by the author. Unlike existing training methods this new technique is able to optimize performance in relation to available memory. Numerically it has equivalent properties to Full Memory BFGS optimization (FM) when there are no restrictions on memory and to FM with periodic reset when memory is limited. Parallel implementations of both the Full and Variable Memory BFGS algorithms are outlined and performance results presented for a PVM target architecture. Copyright (C) 2000 IFAC.
引用
收藏
页码：129 / 134
页数：4
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