Dredging operation yield prediction model analysis method based on BP neural network

A BP neural network and yield forecasting technology, applied in forecasting, data processing applications, computing and other directions, can solve problems such as insufficient utilization of resources

Inactive Publication Date: 2015-03-25
HOHAI UNIV CHANGZHOU
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Problems solved by technology

These data are a true reflection of the actual operation process of dredgers in a specific area and in a specific environment. They have obvi

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  • Dredging operation yield prediction model analysis method based on BP neural network
  • Dredging operation yield prediction model analysis method based on BP neural network
  • Dredging operation yield prediction model analysis method based on BP neural network

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[0060] In order to make the technical means, creative 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.

[0061] see figure 1 , in this embodiment, the analysis method of the cutter suction dredger output prediction model is as follows:

[0062] (1) There are many parameters and variables affecting the output of cutter suction dredgers. First, collect data and determine the analysis variables. The factors affecting the output of cutter suction dredger are shown in Table 1. Yield is a one-dimensional dependent variable Y.

[0063] Table 1 Factors affecting the output of cutter suction dredgers

[0064]

[0065] (2) Preprocessing the raw data

[0066] The result of preprocessing the original data is:

[0067] According to the above step (2), the dependent variable Y' and the independent variable X' can be obtained after data preprocessing of the...

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Abstract

The invention discloses a dredging operation yield prediction model analysis method based on a BP neural network. The dredging operation yield prediction model analysis method based on the BP neural network comprises the following steps that (1) data information influencing dredging operation yield factor variables is collected, p influence factors are determined, and a sample matrix is listed, wherein p is a positive integer; (2) pretreatment is conducted on sample data; (3) a network is established, and a training sample and a testing sample are determined; (4) the established network is trained according to the training sample; (5) according to the testing sample, the established network is tested; (6) the performance of the network is estimated by computing the offset condition between a predicated value and a true value. By the adoption of the dredging operation yield prediction model analysis method based on the BP neural network, a nonlinear mapping function from input to output is achieved, a nonlinear relationship is established between input and output, and an established model is high in fault-tolerant capacity and high in prediction speed; theoretical basis can be laid for optimization study of dredging operation yield, and the purposes of high efficiency, high yield and low energy consumption can be achieved.

Description

technical field [0001] The invention belongs to the technical field of dredging engineering, and in particular relates to a BP neural network-based analysis method for a dredging operation output prediction model. 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. With the development of intelligence in dredging projects, monitoring devices are installed on dredgers to monitor many working parameters offline or online, and these monitoring data are transmitted to the monitoring station through wireless communication. Therefore, in the long-term production process, the dredger has accumulated rich and detailed working condition parameter data. These data are a true reflection of the actual operation process of dredgers in a specific area and in a specific environment...

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Application Information

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IPC IPC(8): G06Q10/04
CPCG06Q10/04
Inventor 李凯凯许焕敏周丰穆乃超宋庆峰
Owner HOHAI UNIV CHANGZHOU
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