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Self-service counting inference method of fractal-sequence parameter estimation

A technology of parameter estimation and statistical inference, applied in the direction of reasoning methods, etc., can solve problems such as lack of physical foundation and insufficient efficiency of differential models

Inactive Publication Date: 2018-10-30
XUCHANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The traditional linear theory lacks a physical basis, and the process-based differential model is slightly inefficient, while the fractal theory provides an effective method for describing such characteristics

Method used

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  • Self-service counting inference method of fractal-sequence parameter estimation
  • Self-service counting inference method of fractal-sequence parameter estimation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0013] (1) Prepare time series x t , using random moving grid method for 1000 resampling;

[0014] (2) Calculate the estimated value of each re-sampling sequence parameter to obtain its self-help probability distribution;

[0015] (3) Use percent position confidence interval to estimate [θ * a / 2 , θ * (1-a) / 2 ] and t-method confidence interval make an estimate;

[0016] (4) Use the percentile acceptance domain and t method The receptive field performs hypothesis testing on it.

[0017] The following takes the fractal Brownian motion sequence as an example to verify. Fractional Brownian motion is a typical Gaussian process with self-similar properties, and has the characteristics of long-range correlation, incremental stationarity and regularity. The scale or self-similar feature is characterized by the parameter H, also known as the Hurst index. H is between [0, 1]. The larger the value of H, the smoother the sequence, and vice versa. When H=1 / 2, the process is Br...

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Abstract

The invention discloses a self-service counting inference method of fractal-sequence parameter estimation. The method specifically includes the following operation steps: (1) preparing a time sequencex<t>, and adopting a random lattice moving method to carry out 1000 times of resampling and 200 times of secondary resampling; (2) calculating estimated values of same-pattern sequence parameters ofeach time, and obtaining self-service probability distribution thereof; (3) using a percentile confidence interval estimate [theta*<a / 2>, theta*<(1-a) / 2>] and a t method confidence interval, as shownin the description, for estimation; and (4) using a percentile acceptance region as shown in the description and a t method acceptance region, as shown in the description, for hypothesis testing thereon.

Description

technical field [0001] The invention relates to a self-help statistical inference method for estimating fractal sequence parameters. Background technique [0002] Scale invariance is a basic feature in nature, which is always associated with the complexity, roughness and irregularity of things. This phenomenon generally exists in nature, such as the pulsation of turbulent flow in pipelines, deep crustal Mineral accumulations, fluctuations in financial market prices and winding coastlines. As for the universality of this law, as Cheng Qiuming pointed out in his research on the evolution of metallogenic systems, various physical, chemical, and biological processes in the earth system are interconnected, influenced, and restricted each other. Constitute a self-organizing structure, so as to realize the continuous cyclic evolution of the system from equilibrium-away from equilibrium-critical state-new equilibrium. The traditional linear theory lacks a physical basis, and the p...

Claims

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Application Information

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IPC IPC(8): G06N5/04
CPCG06N5/041
Inventor 高歆
Owner XUCHANG UNIV