Ship motion forecasting method based on intrinsic plasticity echo state network

An echo state network, ship motion technology, applied in neural learning methods, biological neural network models, special data processing applications, etc. It can improve the forecast accuracy, enrich the expression ability, and improve the security effect.

Active Publication Date: 2020-09-25
SHANGHAI MARITIME UNIVERSITY
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Problems solved by technology

[0008] Therefore, the ship motion prediction method under the existing technology is difficult to achieve good prediction results under complex sea conditions, and the prediction accuracy of the ship motion prediction model is not high, which affects the safety, efficiency and reliability of the ship's offshore operations

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  • Ship motion forecasting method based on intrinsic plasticity echo state network
  • Ship motion forecasting method based on intrinsic plasticity echo state network
  • Ship motion forecasting method based on intrinsic plasticity echo state network

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[0046] The present invention will be further described below in conjunction with the accompanying drawings and simulation examples. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0047] The implementation steps of the wave compensation forecast method based on the echo state network model of local plasticity are as follows:

[0048] Step 1: Data acquisition and processing:

[0049] Divide the simulation time series data of the heave and heave motion of the hull in the sea into a training set with a length of 900s and a test set with a length of 100s;

[0050] Step 2: Build an echo state ne...

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Abstract

The invention provides a ship motion forecasting method based on an intrinsic plasticity echo state network, which comprises the following steps: acquiring historical data of swaying of a ship in a sailing process for modeling, inputting a target moment t, forecasting a motion attitude of the ship at the moment t based on an intrinsic plasticity echo state network model, and adopting correspondingmotion compensation according to a forecast value, wherein the internal plasticity echo state network is a plasticity neural network obtained by optimizing different neurons in the storage pool according to a plasticity rule; the invention is characterized by introducing IP (Intrinsic Plasticity) parameters a and b, constructing an activation function fgen (x) = tanh (ax + b) according to a plasticity rule established by KL divergence (kullback-leibler divergence, KL-divergence), so as to change a single neuron structure in a storage pool; the IP parameter optimization target is that the output of neurons in the reserve pool conforms to the maximum entropy distribution, and the purpose that the reserve pool can capture and carry more input information is achieved.

Description

technical field [0001] The invention belongs to the field of engineering signal processing, and relates to the analysis and prediction of the time series of ships swaying in sea waves, and in particular to a ship motion prediction method based on an inherently plastic echo state network. Background technique [0002] Affected by environmental factors such as waves, wind and ocean currents, ships will inevitably produce swaying motions during their navigation on the sea, which has caused many adverse effects on offshore operations. It is of great significance to model and predict the ship's motion attitude and take corresponding motion compensation measures to improve the safety, efficiency and reliability of offshore operations. [0003] Existing ship motion prediction methods are mainly prediction methods based on linear system theory, and the representative ones are auto-regressive (Auto-regressive, AR) prediction model, Kalman filter model and so on. However, under compl...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/20G06N3/04G06N3/08
CPCG06F30/20G06N3/08G06N3/045Y02T90/00
Inventor 唐刚唐溥黎辅荣胡雄
Owner SHANGHAI MARITIME UNIVERSITY
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