Multi-step Predictions Based on TD-DBP ELMAN Neural Network for Wave Compensating Platform

机译：TD-DBP ELMAN神经网络的波补偿平台多步预测

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The gradient descent momentum and adaptive learning rate TD-DBP algorithm can improve the training speed and stability of Elman network effectively. BP algorithm is the typical supervised learning algorithm, so neural network cannot be trained on-line by it. For this reason, a new algorithm (TD-DBP), which was composed of temporal difference (TD) method and dynamic BP algorithm (DBP), was proposed to overcome the restriction. TD-DBP algorithm can make Elman network train on-line incrementally. Using the collected real time data, the modified TD-DBP algorithm was able to realize direct multi-step predictions for vertical displacement of wave compensating platform.

机译：梯度下降动量和自适应学习率TD-DBP算法可以有效提高Elman网络的训练速度和稳定性。 BP算法是典型的监督学习算法，因此无法对其进行在线训练。为此，提出了一种由时差（TD）方法和动态BP算法（DBP）组成的新算法（TD-DBP），以克服该限制。 TD-DBP算法可以使Elman网络逐步进行在线训练。利用采集到的实时数据，改进的TD-DBP算法能够实现对波补偿平台垂直位移的直接多步预测。

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来源
《Advances in computer science and information engineering.;vol. 2.》|2012年|p.431-436|共6页
会议地点 Zhengzhou(CN);Zhengzhou(CN)
作者
Zhigang Zeng;
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作者单位

Automation College of Guangdong Polytechnic Normal University, Guangzhou, 510665, China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类信息处理（信息加工）;信息处理（信息加工）;
关键词
wave compensating platform; TD-DBP algorithm; multi-step predictions;

机译：波浪补偿平台TD-DBP算法；多步预测;

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机译：基于TD-DBp ELmaN神经网络的波浪补偿平台多步预测

Multi-step Predictions Based on TD-DBP ELMAN Neural Network for Wave Compensating Platform

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