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A Learning Method of Operating Parameters of Trailing Suction Dredger

A technology of trailing suction dredger and operating parameters, which is applied in neural learning methods, biological neural network models, design optimization/simulation, etc. problem, to achieve the effect of high accuracy, simple structure and good model stability

Active Publication Date: 2022-03-04
WUHAN UNIV OF SCI & TECH
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Problems solved by technology

The model studied by Li et al. is not stable enough and the accuracy is not high. The main research focuses on the model between the controllable parameters and the yield rate. Although it is helpful to predict the yield rate with the given parameters, it cannot meet the requirements. The optimal combination of operating parameters is given in the case of production rate
The studies by Braaksma et al. and Li et al. do not provide a general method for similar engineering

Method used

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  • A Learning Method of Operating Parameters of Trailing Suction Dredger
  • A Learning Method of Operating Parameters of Trailing Suction Dredger
  • A Learning Method of Operating Parameters of Trailing Suction Dredger

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example 1

[0060] A Learning Method for Operating Parameters of Treated Dredges. The learning method step of this embodiment is:

[0061] Step 1, 640512 original data groups that are given the dredges of the dredges, which are dredger operators. Determine the feature data and measuring data in each raw data group, the feature data is the pair of ground speed V, compensator pressure F, active head angle θ h And the vertical angle of the lower hub ver Four parameters, measuring data as two parameters of mixture density ρ and mixture flow rate Q.

[0062] Step 2, remove the data set of the mixture density ρ <1.025 in the original data group to obtain 625608 effective data sets of the mixture density ρ ≥ 1.025, and randomly select 600,000 valid data sets (see Table 1 for details). Each valid data group contains feature valid data x and measuring effective data Y. The data set composed of 600,000 feature valid data x is called the feature effective dataset X (see Table 2 for details). The data se...

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Abstract

The invention relates to a learning method for operating parameters of a trailing suction dredger. The technical scheme is: randomly divide m valid data sets into training set, cross-validation set and test set, establish 4-8 multi-layer perceptron models, and obtain all multi-layer perceptrons of the training set by adjusting 5 hyperparameters. The loss function of the model, select the model with the smallest loss function among the loss functions of all the multi-layer perceptron models in the cross-validation set as the optimal model, that is, the implicit model; the explicit model of input and output is established through the implicit model and the regression model. Model, using the ant colony algorithm to solve the expected value c of the yield rate 1 and the expected value of the mixture density c 2 The multi-objective optimization problem is obtained to obtain the optimal parameter combination. The model of the invention has good stability, high accuracy and strong versatility, and can meet the expected value c of a given yield rate. 1 and the expected value of the mixture density c 2 The optimal parameter combination can be achieved under the conditions.

Description

Technical field [0001] The present invention belongs to the technical field of dredge operation parameters. Specifically, there is a learning method of taking the operation parameters of the dredge. Background technique [0002] The main task of taking the dredger is to excavate the sediment from the seabed or riverbed during the sailing, and transport it to the designated area, the mobility and efficiency of the dredger, making the smashing dumping boat becomes a large land. The indispensable machines of reclamation projects, improve the dredging efficiency of suction dredges and reduce the workload of operators becomes the hot problem of current research. [0003] Braaksma et al (J.Braaksma, JBKlaassens, R.Babuska and C.Keizer.Modelpredictive control for optimizing the over dredging performance of a trailingsuction hopper dredger.Proceedings ofthe Eighteenth World Dredging Congress, pp.1263-1274,2007.) Committed Optimize dredging operations by model control, this modeling metho...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08
CPCG06N3/08G06N3/045
Inventor 唐慧柴利黄骏杨君
Owner WUHAN UNIV OF SCI & TECH
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