[1]张金芳,周宇龙,王童雨,等.基于图像处理指标的串级非高斯系统性能评估[J].郑州大学学报(工学版),2025,46(02):75-81.[doi:10.13705/j.issn.1671-6833.2025.02.015]
　ZHANG Jinfang,ZHOU Yulong,WANG Tongyu,et al.Performance Evaluation of Cascade Non-Gaussian Systems Based on Image Processing Index[J].Journal of Zhengzhou University (Engineering Science),2025,46(02):75-81.[doi:10.13705/j.issn.1671-6833.2025.02.015]
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基于图像处理指标的串级非高斯系统性能评估()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年02期
页码:
75-81
栏目:
出版日期:
2025-03-10

文章信息/Info

Title:
Performance Evaluation of Cascade Non-Gaussian Systems Based on Image Processing Index
文章编号:
1671-6833(2025)02-0075-07
作者:
张金芳;  周宇龙;  王童雨;  乔贝贝;  徐慧如
华北电力大学 控制与计算机工程学院,北京 102208
Author(s):
ZHANG Jinfang;  ZHOU Yulong;  WANG Tongyu;  QIAO Beibei;  XU Huiru
School of Control and Computer Engineering, North China Electric Power University, Beijing 102208, China
关键词:
串级控制回路;  性能评估;  非高斯;  图像质量评价;  麻雀算法;  系统辨识
Keywords:
cascade control loops;  performance evaluation;  non-Gaussian;  image quality evaluation;  sparrow algorithm;  system identification
分类号:
TP14
DOI:
10.13705/j.issn.1671-6833.2025.02.015
文献标志码:
A
摘要:
为了对串级系统进行准确快速的性能评估,并改进熵指标平移不变性的缺点,基于图像处理中图像质量评价相关知识提出了新的性能指标;同时,针对传统系统辨识算法不够准确与快速的问题提出了一种混合麻雀算法。首先,基于最小方差理论对串级系统进行丢番图方程分解,获得系统反馈不变量,从而获得系统的评价基准;其次,通过融合了正余弦思想的混合麻雀算法对系统进行辨识,得到系统的模型参数及主副回路的噪声概率密度函数;最后,将所提新指标与熵指标进行混合,得到适用性更好的混合指标。对处在不同噪声下的串级系统进行仿真验证,仿真结果表明:算法的准确性有了明显提升,同时算法运行速度提升了11.98%,新指标的评估结果也比熵指标更加合理。
Abstract:
To accurately and quickly evaluate the performance of cascade systems and address the shortcomings of the translation invariance in entropy metrics, new performance indicators were proposed based on knowledge related to image quality evaluation in image processing. Additionally, a hybrid sparrow algorithm was introduced to tackle the inaccuracy and slowness of traditional system identification algorithms. Firstly, the Diophantine equation decomposition was applied to the cascade system based on the minimum variance theory to obtain system feedback invariants, thereby establishing the system evaluation benchmark. Secondly, a hybrid sparrow algorithm incorporating sine and cosine concepts was used to identify the system, resulting in the model parameters and the noise probability density functions of the primary and secondary loops. Finally, the new indicators were mixed with entropy metrics to create a more applicable hybrid metric. Simulations were conducted on cascade systems in different noise conditions. The simulation results showed a significant improvement in the accuracy of the algorithm, with a speed increase of 11.98%, and the evaluation results of the new indicators were more reasonable than those of the entropy metrics.

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更新日期/Last Update: 2025-03-13