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

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...

AI Technical Summary

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 obvious potential value and are important scientific and technological resources. Unfortunately, these resources are not well utilized at present.

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