Dredging operation yield prediction model building method based on partial least squares regression

A partial least squares and production forecasting technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as low construction efficiency and complex factors affecting production

Inactive Publication Date: 2014-10-29
HOHAI UNIV CHANGZHOU
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Problems solved by technology

The dredging operation of cutter suction dredger is a process of multi-factor interaction and mutual influence, and the factors affecting the output are very many and complex
In the current stage of

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  • Dredging operation yield prediction model building method based on partial least squares regression
  • Dredging operation yield prediction model building method based on partial least squares regression
  • Dredging operation yield prediction model building method based on partial least squares regression

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Embodiment Construction

[0077] In order to make the technical solutions, technical features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments and accompanying drawings.

[0078] according to figure 1 , the model building method step of the present invention is: collect relevant data and determine analysis variable, obtain sample data, carry out standardization process to sample matrix, carry out dimensionality reduction to dependent variable and independent variable high-dimensional data (build matrix and ), extract the eigenvectors (i.e. principal components) that are orthogonal to each other between the independent variable and the dependent variable, and finally establish the linear regression relationship between the dependent variable and the eigenvector of the independent variable (regression prediction equation).

[0079] according to figure 2 , the process of extracting p...

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Abstract

The invention discloses a dredging operation yield prediction model building method based on partial least squares regression. An advanced multivariate regression analysis method is adopted, the high dimensional data space of an independent variable and the high dimensional data space of a dependent variable are projected to corresponding low dimensional characteristic spaces, the mutually orthogonal feature vectors of the independent variable and the dependent variable are obtained respectively, and then the linear regression relation between the feature vectors of independent variable and the dependent variable is built. When the feature vectors are selected, the explanation and predication function of the independent variable on the dependent variable is emphasized, the influence of noise useless to regression is removed, the model comprises the minimum number of the variables, and therefore the model has good robustness and prediction stability. A theoretical foundation can be laid for the optimization study of the dredging operation yield, the aims of high efficiency, high yield and low energy consumption are achieved, and the method has great significance in production predication of a dredger.

Description

technical field [0001] The invention relates to the application of partial least squares regression analysis in a dredging operation output prediction model, and belongs to the field of dredging engineering. Background technique [0002] Dredging engineering is an important project of water conservancy and water transportation engineering. Modern dredging operations mainly rely on dredgers, and output is an important criterion to measure the efficiency of dredgers. The dredging operation of cutter suction dredger is a process of multi-factor interaction and mutual influence, and the factors affecting the output are very many and complex. In the current stage of dredging construction, it is mainly based on manual optimization, relying on past experience to judge the main operating parameters of dredging construction, and the construction efficiency is low. How to ensure reasonable and effective process decision-making, reduce control parameters, and reduce operational compl...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 李凯凯许焕敏穆乃超宋庆峰周玉刚
Owner HOHAI UNIV CHANGZHOU
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