Echo State Network ship motion modeling prediction based on Kalman filter

Peng, Xiuyan; Dong, Huiyuan; Zhang, Biao

doi:10.1109/icma.2017.8015795

Cited by 14 publications

(11 citation statements)

References 9 publications

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“…Other NN models, such as CNNs [38] and echo state networks [39], have shown advantages in neither prediction accuracy nor computational complexity when compared with RNNs. Hence, we do not consider them in this paper.…”

Section: ) Non-nn Prediction Modelsmentioning

confidence: 99%

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Zhang¹,

Hu²,

Zhang³

et al. 2022

Preprint

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This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.<div><br></div><div>A major revision is done, compared to the previous version.</div><div><br></div><div>Abstract: Neural networks (NNs) have been applied to predict complex ship motions for stable ship maneuvering and operations at sea, but the coupling effects of different DoFs are still not effectively modeled in black-box NNs. To this end, we propose a novel deep NN framework that explicitly models the coupling effects through factorization machines, and significantly improves the prediction accuracy without introducing much extra complexity. The proposed framework is also lightweight and frequency-aware, which serves real-time and high-frequency prediction with high accuracy. We experimentally demonstrated that the framework is not only friendly to real-time prediction and online learning, but also interpretable and with great potential to be complemented by common machine learning tricks and go deeper.<br></div><div><br></div>

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Section: ) Non-nn Prediction Modelsmentioning

confidence: 99%

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Zhang¹,

Hu²,

Zhang³

et al. 2022

Preprint

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“…The effectiveness of neural networks for time-series prediction tasks in other fields, such as energy efficiency [14], multi-robot systems [15], and autonomous electric vehicles [16], has been validated. Neural networks, such as simple backpropagation neural networks [17], recurrent neural networks [18], radial basis function neural networks [1] [19], echo state networks [20], extreme learning machines [21], and hybrid models [22] [23], have also been widely utilized in ship motion prediction.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Zhang¹,

Hu²,

Zhang³

et al. 2022

Preprint

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“…The authors in [8] introduced a bat algorithm to overcome the influences of initial random weights, thereby improving the effectiveness and robustness of the ESN prediction system. The work in [9] proposed a Kalman filter to improve ESN predictions by recursively training the network output weight. The authors in [11] and [12] proposed simple cycle reservoirs and cycle reservoirs with jumps so as to shorten the trail session for This research was supported by the U.S. National Science Foundation under Grants IIS-1838021 and CNS-1460316, and by the National Natural Science Foundation of China under Grant 61671086 and 61629101. a specific system.…”

Section: Introductionmentioning

confidence: 99%

“…In [14], the authors proposed a deep self-organizing SHESN so as to construct a large system with a stack of well-trained reservoirs, which improves the prediction ability of the network. However, most of the existing literature such as [8], [9], [11]- [14] only tested the prediction capability of ESNs with various datasets but did not focus on the theoretical analysis. The author in [15] introduced the concept of a short-term memory capacity of ESN to provide a quantitative measure of the prediction capability.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Memory Capacity for Deep Echo State Networks

Liu

Chen

Yin

et al. 2018

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)

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In this paper, the echo state network (ESN) memory capacity, which represents the amount of input data an ESN can store, is analyzed for a new type of deep ESNs. In particular, two deep ESN architectures are studied. First, a parallel deep ESN is proposed in which multiple reservoirs are connected in parallel allowing them to average outputs of multiple ESNs, thus decreasing the prediction error. Then, a series architecture ESN is proposed in which ESN reservoirs are placed in cascade that the output of each ESN is the input of the next ESN in the series. This series ESN architecture can capture more features between the input sequence and the output sequence thus improving the overall prediction accuracy. Fundamental analysis shows that the memory capacity of parallel ESNs is equivalent to that of a traditional shallow ESN, while the memory capacity of series ESNs is smaller than that of a traditional shallow ESN. In terms of normalized root mean square error, simulation results show that the parallel deep ESN achieves 38.5% reduction compared to the traditional shallow ESN while the series deep ESN achieves 16.8% reduction.

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Echo State Network ship motion modeling prediction based on Kalman filter

Cited by 14 publications

References 9 publications

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Analysis of Memory Capacity for Deep Echo State Networks

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