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Chaotic characteristic analysis and non-linear prediction method of run-off

A technique for nonlinear prediction and characteristic analysis

Inactive Publication Date: 2015-09-02
LIUZHOU TEACHERS COLLEGE
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0008] The purpose of the present invention is to provide chaotic characteristic analysis and nonlinear prediction method, which solves the problem that the existing method cannot perform chaotic characteristic analysis and nonlinear prediction for the runoff record data of rivers and seas with a small amount of data

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  • Chaotic characteristic analysis and non-linear prediction method of run-off
  • Chaotic characteristic analysis and non-linear prediction method of run-off
  • Chaotic characteristic analysis and non-linear prediction method of run-off

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Embodiment Construction

[0039] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0040] The invention combines the chaotic characteristic analysis of runoff with the current advanced prediction technology, and the prediction performance of the model is improved. The steps that the present invention adopts are:

[0041] Step 1: Collect runoff data, including all relevant data affecting runoff evolution. Collect runoff data, runoff data is collected once a year, according to the formula of Hong Shizhong and Hong Shiming:

[0042] N min > 2 × ( 27.5 ) D , - - - ( 1 )

[0043] Where D=2, N min >39, when D=3, N min >204, the total collected runoff data is mo...

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Abstract

The invention discloses a chaotic characteristic analysis and non-linear prediction method of run-off. The method includes: collecting more than thirty nine run-off data; subjecting all the run-off data to wavelet transformation to generate a plurality of subsequences; respectively calculating a maximum lyapunov index for each subsequence with a small-data algorithm or other methods; judging the subsequences with the maximum lyapunov indexes being positive numbers as chaos time sequences, and respectively calculating delay time and embedded dimensions of the chaos time sequences; subjecting the corresponding subsequences with chaotic characteristic to phase-space reconstruction according to calculated delay time and the embedded dimensions, and taking reconstruction results as predicated data to be inputted to a neural network model to finish final prediction. The method has the advantage that when the run-off recorded data of rivers and seas are more than thirty nine, chaotic characteristic analysis and non-linear prediction can be performed.

Description

technical field [0001] The invention belongs to the technical field of water conservancy and relates to a chaotic characteristic analysis and nonlinear prediction method of runoff. Background technique [0002] Nonlinear phenomena are common phenomena in nature, and nonlinear systems have properties (similarities) that transcend different disciplines. The similarity of such complex systems lies not in qualitative descriptions, but in practical tests or theoretical details, such as solitons, coherent structures, etc. In recent years, nonlinear science has made considerable progress, especially the establishment of chaos theory, which has shown new horizons for various disciplines. Chaos is a cutting-edge topic and academic hotspot today. It reveals the ubiquitous complexity, the unity of order and disorder, and the unity of determinism and randomness in nature and human society, broadening people's horizons and deepening their understanding of cognition of the objective wor...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 丁红
Owner LIUZHOU TEACHERS COLLEGE