# Fault Recovery Path Analysis of a Software Dynamic Image Based on a Fuzzy Control Algorithm

^{1}

^{2}

## Abstract

**:**

## 1. Introduction

## 2. Software Dynamic Image Fault Sample Information Acquisition and Preprocessing

#### 2.1. Software Dynamic Image Fault Code Detects Sample Information Acquisition

#### 2.2. Multidimensional Wavelet Decomposition of Fault Recovery Path

## 3. Optimization of the Software Dynamic Image Fault Detection Algorithm

#### 3.1. High-Order Spectral Feature Extraction of the Software Dynamic Image Fault Signal

_{1}(t) and a

_{2}(t), for wavelet entropy estimation of software dynamic image fault characteristics are determined by the following formula.

#### 3.2. Automatic Identification and Detection of Software Dynamic Image Fault Locations

## 4. Simulation Experiment and Performance Analysis

## 5. Conclusions

## Funding

## Conflicts of Interest

## References

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**Figure 5.**Information acquisition samples of embedded software under dynamic image fault state of three kinds of software.

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**MDPI and ACS Style**

Zhang, T.
Fault Recovery Path Analysis of a Software Dynamic Image Based on a Fuzzy Control Algorithm. *Symmetry* **2020**, *12*, 897.
https://doi.org/10.3390/sym12060897

**AMA Style**

Zhang T.
Fault Recovery Path Analysis of a Software Dynamic Image Based on a Fuzzy Control Algorithm. *Symmetry*. 2020; 12(6):897.
https://doi.org/10.3390/sym12060897

**Chicago/Turabian Style**

Zhang, Tuqian.
2020. "Fault Recovery Path Analysis of a Software Dynamic Image Based on a Fuzzy Control Algorithm" *Symmetry* 12, no. 6: 897.
https://doi.org/10.3390/sym12060897