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A fuzzy c-means clustering method for identifying chaos with small amount of data

A mean clustering and small data volume technology, applied in calculation models, based on specific mathematical models, calculations, etc., to achieve accurate calculation results, simple process, effective and accurate weather forecasting

Active Publication Date: 2017-05-17
CHONGQING INST OF GREEN & INTELLIGENT TECH CHINESE ACADEMY OF SCI
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Problems solved by technology

[0005] In view of this, the purpose of the present invention is to provide a method of fuzzy C-means clustering small amount of data to identify chaos, the method is used to solve the problem of calculating the maximum Lyapunov exponent in an actual chaotic system, and adopts a small amount of data algorithm to obtain divergence shrinkage The degree index set, combined with the fuzzy C-means clustering algorithm to classify the data, not only avoids the artificial selection of linear regions, but also improves the calculation accuracy

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  • A fuzzy c-means clustering method for identifying chaos with small amount of data
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  • A fuzzy c-means clustering method for identifying chaos with small amount of data

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Embodiment Construction

[0035] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0036] Lorentz systems are used to describe many different physical phenomena, such as phenomena such as weather, convection, and ramp waves, and devices such as water turbines, generators, and laser machines. If x represents the convective fluid motion, y represents the horizontal temperature change, and z represents the vertical temperature change, then the dimensionless Lorenz equation reflecting convection can be written as:

[0037]

[0038] The advantages of the present invention are illustrated below with specific implementation examples. The details are as follows: figure 1 Shown:

[0039] Step 1: Use the fourth-order Runge-Kutta algorithm to integrate the Lorenz equation, the integration step size is 0.01, and the initial value of the integration is [-1 0 1];

[0040] And filter and sample it, sampling frequency f=100, obtain...

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Abstract

The invention discloses a small data size chaos identifying method through fuzzy C-means cluster and belongs to the field of signal processing. The method comprises the following steps: signals are subjected to smoothing and sampling in the practical engineering application to obtain time sequence data; the small data size algorism is adopted to process the sampled time sequence data to obtain divergence and shrinkage degree index collection; the fuzzy C-means cluster algorism is adopted to divide the divergence and shrinkage degree index collection into saturated data and unsaturated data and the unsaturated data is reserved; the unsaturated data is subjected to second order difference; the obtained data is divided into three kinds of data, namely positive fluctuation data, negative fluctuation data and zero fluctuation data through the fuzzy C-means cluster algorism; the longest continuous natural number interval in the zero fluctuation data is selected to set an error range according to the precision requirement and effective zero fluctuation data is reserved; the statistics method is adopted to select the optimal solution so as to calculate the maximum Lyapunov index. The method has great significance to nonlinear application.

Description

technical field [0001] The invention provides a method for identifying chaos by fuzzy C-means clustering with small amount of data, and relates to the technical fields of signal processing, machinery and medical diagnosis, meteorological prediction, sea surface target detection and the like. Background technique [0002] At present, the identification of chaotic signals has been widely used in astrophysics, hydrology, medicine, bioengineering and other research. When diagnosing and describing chaotic signals, Lyapunov exponent is an important index to judge the system is in chaotic state. [0003] Among the existing methods, the main methods for calculating Lyapunov index are Wolf method, Jacobian method, P-norm method and small data amount method. The small data amount method has the advantages of simple calculation process, fast operation speed and strong robustness, and is currently the most widely used method. [0004] In the calculation process of small data volume, i...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N7/00
Inventor 周双冯勇吴文渊杨文强
Owner CHONGQING INST OF GREEN & INTELLIGENT TECH CHINESE ACADEMY OF SCI
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