A New Computational Approach to the Levenberg-Marquardt Learning Algorithm

被引：0
|
作者：
Bilski, Jaroslaw [1 ]
Kowalczyk, Barosz [1 ]
Smolag, Jacek [1 ]
机构：
[1] Czestochowa Tech Univ, Inst Computat Intelligence, Czestochowa, Poland
来源：
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2022, PT I | 2023年 / 13588卷
关键词：
Neural network learning algorithm; Levenberg-marquardt learning algorithm; Vector computations; Approximation; Classification; NEURO-FUZZY SYSTEMS; PARALLEL REALIZATION; NONLINEAR REGRESSIONS; NETWORKS; IDENTIFICATION;
D O I：
10.1007/978-3-031-23492-7_2
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A new parallel computational approach to the Levenberg-Marquardt learning algorithm is presented. The proposed solution is based on the AVX instructions to effectively reduce the high computational load of this algorithm. Detailed parallel neural network computations are explicitly discussed. Additionally obtained acceleration is shown based on a few test problems.
引用
收藏
页码：16 / 26
页数：11
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