Deep learning model based on complex network and its application in measurement signal analysis

A complex network and deep learning technology, applied in the field of deep learning models, can solve the problem that the classification effect cannot be very accurate.

Active Publication Date: 2018-11-30
钧晟(天津)科技发展有限公司
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Problems solved by technology

However, most of the training models in the past were shallow, and the classification effect could not be very accurate.

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  • Deep learning model based on complex network and its application in measurement signal analysis
  • Deep learning model based on complex network and its application in measurement signal analysis
  • Deep learning model based on complex network and its application in measurement signal analysis

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Embodiment Construction

[0043]The complex network-based deep learning model of the present invention and its application in measurement signal analysis will be described in detail below in conjunction with the embodiments and accompanying drawings.

[0044] The deep learning model based on the complex network of the present invention establishes the complex network by adopting the method of visual graph for the measurement signal, extracts a large number of indicators of the complex network, and uses it as the input of the deep learning model, so that the model can pass the training of a large number of samples and unsupervised And supervised learning, get a deep learning model based on complex networks.

[0045] The establishment method of the deep learning model based on complex network of the present invention, comprises the steps:

[0046] 1) Set the principle of constructing a complex network for the measurement signal through the method of visualization:

[0047] The time series obtained by me...

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Abstract

A deep learning model based on complex networks and its application in measurement signal analysis: set the principle of constructing complex networks for measurement signals through the method of visual graphs, and establish complex networks of visual graphs; use visual graphs for each channel measurement signal Methods The complex network was established, and for each network, the following indicators were extracted: node aggregation coefficient, node degree, node betweenness, node proximity centrality, and node eigenvector centrality; a deep learning model was established. The vertical oil-water two-phase flow experiment is carried out to obtain the measurement signal by using the circularly excited conductivity sensor; the ratio of the oil phase and the water phase is fixed, and the flow rate of the oil phase and the water phase is changed for the experiment. The present invention constructs a complex network for the measurement signal, uses a large number of indicators of the complex network to form samples, and uses a layer-by-layer optimization mechanism to combine supervised learning and unsupervised learning to obtain a deep learning model. It can be used to predict and classify measurement signals of unknown categories.

Description

technical field [0001] The invention relates to a deep learning model. In particular, it relates to a complex network-based deep learning model for multi-channel measurement signals obtained by sensors and its application in measurement signal analysis. Background technique [0002] The analysis method of measurement signal has been widely used in many fields, and it is of great significance for revealing the intrinsic characteristics of complex systems, such as oil-water two-phase flow system. Oil-water two-phase flow exists widely in oil exploration and transportation industries. In the oil-water two-phase flow system, the distribution of each phase changes with time and space, forming different flow patterns, which are called flow patterns. The flow pattern of two-phase flow is complex and changeable, and it is difficult to accurately capture local flow information, which makes the measurement of two-phase flow flow parameters such as phase holdup present many difficult...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/08G06K9/00
CPCG06N3/088G06F2218/12
Inventor 高忠科杨宇轩薄云王新民董长松
Owner 钧晟(天津)科技发展有限公司
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