Multi-dimensional visual multi-source heterogeneous data multi-layer DRNN deep fusion method

A technology of multi-source heterogeneous data and fusion method, which is applied in the field of deep fusion of multi-source heterogeneous data and multi-layer DRNN, can solve the problems that the direct display of heterogeneous data cannot be guaranteed

Active Publication Date: 2019-11-29
沈阳瑞初科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the current visualization technology for heterogeneous in

Method used

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  • Multi-dimensional visual multi-source heterogeneous data multi-layer DRNN deep fusion method
  • Multi-dimensional visual multi-source heterogeneous data multi-layer DRNN deep fusion method
  • Multi-dimensional visual multi-source heterogeneous data multi-layer DRNN deep fusion method

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Embodiment Construction

[0040] Such as figure 1 , figure 2 and image 3 As shown, the multi-dimensional visualized multi-source heterogeneous data multi-layer DRNN deep fusion method is characterized in that: the method steps include:

[0041] Step 1: Establish DRNN neural network;

[0042] For multi-source heterogeneous data, establish a recursive self-learning network based on deep neural network; establish a deep learning model, and form a recursive network at each layer. The recursive network uses deep self-learning to generate a DRNN network; build a DRNN network close to the underlying data There is no link between the nodes in the layer, and fuzzy fusion judgment is used in the farthest part, and the corresponding generation model is obtained by increasing the number of hidden layers through forward self-encoding.

[0043] The specific method of establishing a recursive self-learning network based on deep warp network is:

[0044] Using a layered architecture, each layer adaptively establ...

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Abstract

The invention belongs to the field of artificial intelligence education, and particularly relates to a multi-dimensional visual multi-source heterogeneous data multi-layer DRNN deep fusion method. Themethod comprises the following steps: 1, establishing a DRNN neural network; inputting the multi-source heterogeneous data into a DRNN neural network to form a DRNN training template; 3, establishingforward output of a single-layer neuron individual of the DRNN network; wherein the single-layer network is used as an activated fusion function; 4, establishing weight reconstruction and correctionof the DRNN network; wherein the correction exists as a fusion function; and step 5, obtaining feature output through data feature fusion in a data fusion layer of the DRNN. The invention provides a fusion architecture, which can perform feature level fusion for different multi-source heterogeneous data.

Description

Technical field: [0001] The invention belongs to the field of artificial intelligence education, and in particular relates to a multi-dimensional visualized multi-source heterogeneous data multi-layer DRNN deep fusion method. Background technique: [0002] Visualization is the theory, method and technology of using computer graphics and image processing technology to convert data into graphics or images and display them on the screen for interactive processing. It involves many fields such as computer graphics, image processing, computer vision, and computer-aided design, and has become a comprehensive technology for studying a series of issues such as data representation, data processing, and decision analysis. The virtual reality technology that is currently developing rapidly is also based on the visualization technology of graphics and images. [0003] The development of computer graphics in recent years has led to the formation of three-dimensional representation techn...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/044G06N3/045G06F18/253
Inventor 罗晓东
Owner 沈阳瑞初科技有限公司
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