Real-time prediction of waves using neural networks trained by particle swarm optimization

D. Gopinath, GS Dwarakish
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引用次数: 15

Abstract

This work investigates the strength of artificial neural network that is trained by an optimization technique called particle swarm optimization in the task of time series prediction of weekly and monthly significant wave heights. The suggested approach has been implemented at the location of New Mangalore Port in India. Three years of wave data measured during 2005–2007 are analyzed. It is found that the network trained with the help of the particle swarm optimization produces more accurate predictions of the significant wave heights and further with lesser amount of data than the traditionally trained feed-forward back-propagation network.
利用粒子群优化训练的神经网络对波浪进行实时预测
这项工作研究了人工神经网络的强度,这种神经网络是由一种称为粒子群优化的优化技术训练的,它在时间序列预测周和月显著波高的任务中。建议的方法已经在印度新芒格洛尔港实施。分析了2005-2007年3年的波浪测量数据。研究发现,与传统的前馈反向传播网络相比,利用粒子群优化方法训练的网络可以更准确地预测有效波高,并且数据量更少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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