Exploration and Mining Learning Robot of Autonomous Marine Resources Based on Adaptive Neural Network Controller
To study the autonomous learning model of the learning robot for marine resource exploration, an adaptive neural network controller was applied. The motion characteristics of autonomous learning robots were identified. The mathematical model of the multilayer forward neural network and its improved...
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Online Access: | https://doi.org/10.2478/pomr-2018-0115 |
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doaj-28d8e23bbf544da1b1b50631052ef9af2021-09-05T14:01:07ZengSciendoPolish Maritime Research2083-74292018-12-0125s3788310.2478/pomr-2018-0115pomr-2018-0115Exploration and Mining Learning Robot of Autonomous Marine Resources Based on Adaptive Neural Network ControllerPan Lisheng0School of Management, Hefei Uniwersity Of Technology,Hefei, ChinaTo study the autonomous learning model of the learning robot for marine resource exploration, an adaptive neural network controller was applied. The motion characteristics of autonomous learning robots were identified. The mathematical model of the multilayer forward neural network and its improved learning algorithm were studied. The improved Elman regression neural network and the composite input dynamic regression neural network were further discussed. At the same time, the diagonal neural network was analysed from the structure and learning algorithms. The results showed that for the complex environment of the ocean, the structure of the composite input dynamic regression network was simple, and the convergence was fast. In summary, the identification method of underwater robot system based on neural network is effective.https://doi.org/10.2478/pomr-2018-0115adaptive neural networkmarine resourceslearning robot |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pan Lisheng |
spellingShingle |
Pan Lisheng Exploration and Mining Learning Robot of Autonomous Marine Resources Based on Adaptive Neural Network Controller Polish Maritime Research adaptive neural network marine resources learning robot |
author_facet |
Pan Lisheng |
author_sort |
Pan Lisheng |
title |
Exploration and Mining Learning Robot of Autonomous Marine Resources Based on Adaptive Neural Network Controller |
title_short |
Exploration and Mining Learning Robot of Autonomous Marine Resources Based on Adaptive Neural Network Controller |
title_full |
Exploration and Mining Learning Robot of Autonomous Marine Resources Based on Adaptive Neural Network Controller |
title_fullStr |
Exploration and Mining Learning Robot of Autonomous Marine Resources Based on Adaptive Neural Network Controller |
title_full_unstemmed |
Exploration and Mining Learning Robot of Autonomous Marine Resources Based on Adaptive Neural Network Controller |
title_sort |
exploration and mining learning robot of autonomous marine resources based on adaptive neural network controller |
publisher |
Sciendo |
series |
Polish Maritime Research |
issn |
2083-7429 |
publishDate |
2018-12-01 |
description |
To study the autonomous learning model of the learning robot for marine resource exploration, an adaptive neural network controller was applied. The motion characteristics of autonomous learning robots were identified. The mathematical model of the multilayer forward neural network and its improved learning algorithm were studied. The improved Elman regression neural network and the composite input dynamic regression neural network were further discussed. At the same time, the diagonal neural network was analysed from the structure and learning algorithms. The results showed that for the complex environment of the ocean, the structure of the composite input dynamic regression network was simple, and the convergence was fast. In summary, the identification method of underwater robot system based on neural network is effective. |
topic |
adaptive neural network marine resources learning robot |
url |
https://doi.org/10.2478/pomr-2018-0115 |
work_keys_str_mv |
AT panlisheng explorationandmininglearningrobotofautonomousmarineresourcesbasedonadaptiveneuralnetworkcontroller |
_version_ |
1717810761053503488 |