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Dredging process intelligent decision analysis method based on fuzzy neural control system

A control system and fuzzy neural technology, applied in the field of dredging engineering, can solve the problems of high energy consumption, low degree of dredging automation, high emissions, etc., and achieve the effect of strong adaptability and robustness, and high degree of automation of dredging operations

Inactive Publication Date: 2015-11-11
HOHAI UNIV CHANGZHOU
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Problems solved by technology

[0002] As an underwater operation, dredging has many process control parameters, and at present, the degree of automation of dredging in China is not high, and manual operation is still the main method
Even for experienced operators, due to the complex factors affecting the mud flow state, such as mud concentration, mud flow velocity, sediment particle size, sedimentation velocity of different sediments and pipeline performance, etc., and the interaction of various factors makes qualitative testing relatively difficult
As a result, dredging production has been in a state of low production, low efficiency, high energy consumption and high emissions

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  • Dredging process intelligent decision analysis method based on fuzzy neural control system
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  • Dredging process intelligent decision analysis method based on fuzzy neural control system

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[0035] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0036] like figure 1 As shown, the intelligent decision-making analysis method of dredging process based on fuzzy neural control system includes the following steps:

[0037] Step 1, collecting data on relevant decision-making parameters affecting the construction process of dredging operations.

[0038] The relevant decision-making parameters affecting the construction process of dredging operations need to be obtained based on construction experience. The decision-making parameters collected here include mud concentration, reamer speed, mud pump speed, pipeline flow rate, reamer traverse speed, reamer cutting mud thickness, reamer Knife advance distance, reamer depth, trolley travel, pipelin...

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Abstract

The invention discloses a dredging process intelligent decision analysis method based on a fuzzy neural control system. The method comprises the steps that 1, data influencing related decision parameters of a dredging construction technology are collected; 2, a matlab principal component analysis method is used to find out the feature thresholds of several decision parameters with the maximum contribution rate; 3, a knowledge base is established; 4, a fuzzy neural control system mechanism is selected and artificial neurons are established, and the number of hidden layers of a neural network is determined according to the dredging complexity; and 5, the fuzzy neural control system is established to carry out assisted decision on dredging construction. According to the invention, the space complexity of the neural network and fuzzy control time complexity are fused, so that the degree of dredging automation is high.

Description

technical field [0001] The invention relates to an intelligent decision-making analysis method for dredging technology based on a fuzzy neural control system, which belongs to the field of dredging engineering. Background technique [0002] As an underwater operation, dredging has many process control parameters, and at present, the degree of automation of dredging in China is not high, and manual operation is still the main method. Even for experienced operators, due to the complex factors affecting the mud flow state, such as mud concentration, mud flow velocity, sediment particle size, sedimentation velocity of different sediments and pipeline performance, etc., and the interaction of various factors makes qualitative testing relatively difficult . As a result, dredging production has been in a state of low production, low efficiency, high energy consumption and high emissions. Therefore, it is particularly urgent to improve the degree of dredging automation. [0003] ...

Claims

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Application Information

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IPC IPC(8): G05B13/02
Inventor 王祥冰许焕敏李凯凯穆乃超宋庆锋孔德强
Owner HOHAI UNIV CHANGZHOU
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