Deep learning cellular automaton simulation analysis method based on geographic information system

A geographic information system and cellular automata technology, applied in the field of data simulation, can solve problems such as problems to be improved, and achieve the effect of rapid analysis and evaluation
CN104850677AInactive Publication Date: 2015-08-19WUHAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
WUHAN UNIV
Publication Date
2015-08-19
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a deep learning cellular automaton simulation analysis method based on a geographic information system. Comprehensive simulation analysis is performed on data through comprehensive use of the geographic information system, deep learning, a cellular automaton and the like. The method is based on the technologies of geographic information system, database and the like; the conversion rule of the cellular automaton is mined and extracted through deep learning, and a model is built; and the design mode of centralized management and maintenance is adopted for performing unified control and management on information in a research region. The functions of illustrated integrated maintenance management, comprehensive inquiry, spatial analysis and simulation analysis are realized. The method has the beneficial effects that a method and technology of simulation analysis are researched through comprehensive use of the geographic information system, deep learning, the cellular automaton and a spatial analysis technology, and innovation is realized on a theoretical method in the field.
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Description

Technical field

[0001] The invention belongs to the field of data simulation technology and relates to a deep learning cellular automaton simulation analysis method based on a geographic information system. Background technique

[0002] Nowadays, deep learning has become a popular field of big data and artificial intelligence. This method builds a hierarchical model structure similar to the human brain, and extracts features from the bottom to the top of the input data step by step, so that it can well establish the features from the bottom to the top. High-level mapping relationships. Deep learning methods can achieve complex function approximation by learning a deep nonlinear network structure, characterize the distributed representation of input data, and demonstrate a powerful ability to learn the essential characteristics of a data set from a small number of samples.

[0003] The geographical information system is a spatial information analysis and simulation system, w...

Claims

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