Parallel and separable recursive Levenberg-Marquardt training algorithm

被引：0

作者：

Asirvadam, VS ^{[1
]}

McLoone, SF ^{[1
]}

Irwin, GW ^{[1
]}

机构：

[1] Queens Univ Belfast, Sch Elect & Elect Engn, Intelligent Syst & Control Res Grp, Belfast, Antrim, North Ireland

来源：

NEURAL NETWORKS FOR SIGNAL PROCESSING XII, PROCEEDINGS | 2002年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A novel decomposed recursive Levenberg Marquardt (RLM) algorithm is derived for the training of feedforward neural networks. By neglecting interneuron weight correlations the recently proposed RLM training algorithm can be decomposed at neuron level enabling weights to be updated in an efficient parallel manner. A separable least squares implementation of decomposed RLM is also introduced. Experiment results for two nonlinear time series problems demonstrate the superiority of the new training algorithms.

引用

页码：129 / 138

页数：10

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