Sample period accurate estimation method based on linear regression and remainder period

A linear regression and precise estimation technology, applied in complex mathematical operations, design optimization/simulation, etc., can solve problems such as long-term accumulation of sequences, deviations in estimated cycle numbers, low accuracy of sequence cycle estimates, and low data utilization. Achieve the effects of reducing computational complexity, increasing the number of samples, and improving computational accuracy

Active Publication Date: 2020-02-21
SOUTHWEST CHINA RES INST OF ELECTRONICS EQUIP
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem that in the case of noise and discontinuous samples, the current sequence period estimation method cannot achieve long-term accum

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Sample period accurate estimation method based on linear regression and remainder period
  • Sample period accurate estimation method based on linear regression and remainder period
  • Sample period accurate estimation method based on linear regression and remainder period

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0044] like figure 1 As shown, this embodiment provides an accurate estimation method of sample period based on linear regression and remainder period, including the following steps:

[0045] S1. Screen the given data to obtain a screened sample X, and the number of samples is n. Specifically, an ordered sample is obtained by arranging the initial sequence, and the Euclidean distance is used to eliminate the error value and discrete value of the ordered sample, and a filtered sample X is obtained.

[0046] S2. Calculate the accumulated residual error Δ and the number of cycles M of the sample X according to the sample X and the initial period t, and perform linear correction on the accumulated residual error Δ and the number of cycles M. Due to the periodic change of the remainder of the sample X, each time a period of the remainder passes, the accumulated residual error Δ will appear nonlinear, so the accumulated residual error Δ and the number of cycles M are corrected base...

Embodiment 2

[0065] This embodiment is on the basis of Embodiment 1:

[0066] In step S4 of Embodiment 1, this embodiment first calculates the linear relationship between the corrected number M' and the corrected residual accumulated error Δ' to obtain b 1 , and then calculate the linear relationship between the sample X and the corrected residual cumulative error Δ' to obtain b 2 , so as to obtain the precise measurement period t r :

[0067] t r =b 1 / b 2 .

Embodiment 3

[0069] This embodiment is on the basis of Embodiment 1:

[0070] In order to prove the validity of the method for accurately estimating the sample period provided in Example 1, a numerical simulation test was carried out in this example, according to figure 1 The processing flow shown is processed.

[0071] The simulation uses a sample sequence with a period of 1, and adds a certain amount of noise. figure 2 is the sparse sequence sample distribution in , image 3 is the error accumulation of the samples obtained on the basis of the sequence with the existing coarse period, Figure 4 is the error accumulation of the samples obtained based on the calculated fine-measuring period. figure 2 The middle abscissa shows the serial number of the sample, and the ordinate is the sequence sample; image 3 , 4 The middle abscissa is the sequence sample, and the ordinate is the corresponding accumulated error.

[0072] like figure 2 As shown, the distribution of samples is sparse...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a sample period accurate estimation method based on linear regression and a remainder period, and the method comprises the following steps: 1) screening given data to obtain ascreened sample X; 2) calculating a remainder accumulation error delta and a period number M of the sample X according to the sample X and the initial period t, and performing linear correction; 3) performing linear fitting on the sample X and the corrected remainder accumulation error delta ', judging a linear relationship between the sample X and the corrected remainder accumulation error delta', and if the linear relationship is met, executing the next step; and 4) performing linear regression on the sample X and the correction number M 'to obtain a final fitting sequence and an accurate measurement period tr. According to the method, the characteristic that the sensitivity of remainders is higher than that of an original sequence is utilized, estimation errors of the sequence period number are eliminated, the number of available sequences is increased, and long-time sample accumulation is achieved. Based on a linear change rule of remainders, a sample sequence is fitted by using linear regression, so that the estimation performance of a sequence periodic process is improved.

Description

technical field [0001] The invention relates to the technical field of sequence period estimation, in particular to an accurate estimation method of sample period based on linear regression and remainder period. Background technique [0002] Sequence Periodic sequence is a ubiquitous process, such as the periodicity of electromagnetic waves and the periodic law of atmospheric motion. Understanding and applying the periodic law is of great significance in the field of pattern recognition and communication, and can be widely used in space exploration, geographic mapping and environmental protection. [0003] For the period calculation problem, among the traditional methods, Fourier transform (FFT) is the simplest and most direct method: converting the sample sequence to the frequency domain can quickly and effectively extract the data period, but for small samples such as sparse and noisy data, the computational precision of the FFT is not sufficient to apply. Methods such a...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F30/20G06F17/18
CPCG06F17/18
Inventor 吕文超黄辰史小伟张蔚
Owner SOUTHWEST CHINA RES INST OF ELECTRONICS EQUIP
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap