Performance Evaluation of Cascade Non-Gaussian Systems Based on Image Processing Index

NAVIGATE

Table of Contents

STATISTICS

Viewed139

Downloads93

Performance Evaluation of Cascade Non-Gaussian Systems Based on Image Processing Index

[HTML] PDF下载 (93)

[1]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]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 46 Number of periods: 2025 02 Page number: 75-81 Column: Public date: 2025-03-10

Title:: Performance Evaluation of Cascade Non-Gaussian Systems Based on Image Processing Index

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

Keywords:: cascade control loops; performance evaluation; non-Gaussian; image quality evaluation; sparrow algorithm; system identification

CLC:: TP14

DOI:: 10.13705/j.issn.1671-6833.2025.02.015

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.

References:: [1]　PIMENTEL M R, MUNARO C J. Performance monitoring and retuning for cascaded control loops[C]∥The 15th IEEE International Conference on Industry Applications (INDUSCON). Piscataway: IEEE, 2023: 647-654.
[2]　TAN S B, YU X, WANG H H, et al. Performance assessment of cascade control systems based on LQG benchmark[C]∥2016 Chinese Control and Decision Conference (CCDC). Piscataway:IEEE, 2016: 49-52.
[3]　JIA Y, ZHOU J L, LI D Z. Performance assessment of cascade control loops with non-Gaussian disturbances[C]∥ 2018 Chinese Automation Congress (CAC). Piscataway: IEEE, 2018: 2451-2456.
[4]　刘阳, 王亚刚. 最小熵基准的并行串级控制系统的性能评估[J]. 控制工程, 2019, 26(10): 1899-1904. LIU Y, WANG Y G. Performance assessment of parallel cascade control system based on minimum entropy[J]. Control Engineering of China, 2019, 26(10): 1899-1904.
[5]　ZHANG Q, WANG Y G, LEE F F, et al. Improved renyi entropy benchmark for performance assessment of common cascade control system[J]. IEEE Access, 1074, 7: 6796-6803.
[6]　黄国豆. 非高斯随机系统控制回路性能评估方法的研究[D]. 北京: 华北电力大学, 2022. HUANG G D. Research on performance evaluation method of control loop of non-Gaussian stochastic system[D]. Beijing: North China Electric Power University, 2022.
[7]　ZHANG H, ZHOU J L, WANG J. Performance assessment of non-Gaussian systems based on double error entropy minimization[C]∥The IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS). Piscataway:IEEE, 2019: 1177-1182.
[8]　ZHANG J F, HUANG G D, ZHANG L. Generalized correntropy criterion-based performance assessment for nonGaussian stochastic systems[J]. Entropy, 2021, 23(6): 764.
[9]　HORÉ A, ZIOU D. Image quality metrics: PSNR vs. SSIM[C]∥The 20th International Conference on Pattern Recognition. Piscataway:IEEE, 2010: 2366-2369.
[10]胡文, 景玉海. 基于KL散度与JS散度相似度融合推荐算法[J]. 哈尔滨商业大学学报(自然科学版), 2020, 36(1): 48-53. HU W, JING Y H. Recommendation algorithm based on fusion of KL divergence and JS divergence similarity[J]. Journal of Harbin University of Commerce (Natural Sciences Edition), 2020, 36(1): 48-53.
[11] JIANG H X, ZHOU J L, FAN S Y, et al. Analysis on control performance assessment based on minimum entropy[C]∥2018 IEEE 4th International Conference on Computer and Communications (ICCC). Piscataway:IEEE, 2018: 2731-2735.
[12]高岳林, 杨钦文, 王晓峰, 等. 新型群体智能优化算法综述[J]. 郑州大学学报(工学版), 2022, 43(3): 21-30. GAO Y L, YANG Q W, WANG X F, et al. Overview of new swarm intelligent optimization algorithms[J]. Journal of Zhengzhou University (Engineering Science), 2022, 43(3): 21-30.
[13]薛涛, 张安杰. 多策略改进的麻雀搜索算法及应用[J]. 西安工程大学学报, 2023, 37(2): 96-104. XUE T, ZHANG A J. Improved sparrow search algorithm based on multiple strategies and its application[J]. Journal of Xi’an Polytechnic University, 2023, 37(2): 96-104.
[14] MIRJALILI S. SCA: a sine cosine algorithm for solving optimization problems[J]. Knowledge-Based Systems, 2016, 96: 120-133.
[15] ZHANG J, WANG J S. Improved salp swarm algorithm based on Levy flight and sine cosine operator[J]. IEEE Access, 2020, 8: 99740-99771.

Similar References:

Memo

Last Update: 2025-03-13