New dynamic error spectrum for performance evaluation

被引：1

作者：

Peng, Weishi ^{[1
]}

Fang, Yangwang ^{[2
]}

机构：

[1] People Armed Police Engn Univ, Sch Equipment Management & Support, Xian 710086, Shaanxi, Peoples R China

[2] Northwestern Polytech Univ, Unmanned Syst Res Inst, Xian, Shaanxi, Peoples R China

来源：

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2023年 / 44卷 / 01期

基金：

中国国家自然科学基金; 美国国家科学基金会;

关键词：

Performance evaluation; decision support systems; parameter estimation; new dynamic error spectrum; KALMAN FILTER; ALGORITHMS;

D O I：

10.3233/JIFS-221958

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In performance evaluation, the widely used root-mean-square error is easily affected by large error terms and is also an incomprehensive measure. Therefore, the error spectrum as a comprehensive measure was proposed for parameter estimation. However, error spectrum (ES) is a three-dimension plot (among ES, r axis and time t axis) in the whole time horizon in dynamic evaluation system, which is not intuitive and easy to be analyzed. To smooth this, a new dynamic error spectrum (NDES) is proposed in dynamic evaluation system in this paper. Firstly, the NDES is defined for EPE in dynamic systems. Secondly, the computation method is proposed to calculate the NDES. Thirdly, several nice properties of NDES are presented for dynamic system performance evaluation. Finally, the effectiveness of the proposed new dynamic error spectrum is verified by a numerical example.

引用

页码：775 / 782

页数：8

共 29 条

[1] Sustainable Management of Digital Transformation in Higher Education: Global Research Trends [J].

Abad-Segura, Emilio ;

Gonzalez-Zamar, Mariana-Daniela ;

Aand, Juan C. Infante-Moro ;

Ruiperez Garcia, German .

SUSTAINABILITY, 2020, 12 (05)

[2] Robust Distributed Parameter Estimation of Nonlinear Systems With Missing Data Over Networks [J].

Chen, Sicong ;

Liu, Ying .

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2020, 56 (03) :2228-2244

[3] Lightweight Extended Kalman Filter for MARG Sensors Attitude Estimation [J].

Dai, Zeyang ;

Jing, Lei .

IEEE SENSORS JOURNAL, 2021, 21 (13) :14749-14758

[4] Robust Power System State Estimation With Minimum Error Entropy Unscented Kalman Filter [J].

Dang, Lujuan ;

Chen, Badong ;

Wang, Shiyuan ;

Ma, Wentao ;

Ren, Pengju .

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2020, 69 (11) :8797-8808

[5] Deep Learning for Channel Estimation: Interpretation, Performance, and Comparison [J].

Hu, Qiang ;

Gao, Feifei ;

Zhang, Hao ;

Jin, Shi ;

Li, Geoffrey Ye .

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2021, 20 (04) :2398-2412

[6] Evaluation of estimation algorithms. Part 1: Incomprehensive measures of performance [J].

Li, X. Rong ;

Zhao, Zhanlue .

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2006, 42 (04) :1340-1358

[7]

Li XQ, 2017, 2017 20TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), P574

[8] Protocol-Based Unscented Kalman Filtering in the Presence of Stochastic Uncertainties [J].

Liu, Shuai ;

Wang, Zidong ;

Chen, Yun ;

Wei, Guoliang .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2020, 65 (03) :1303-1309

[9]

Liu Y, 2013, 2013 16TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), P477

[10] Dynamic error spectrum for estimation performance evaluation: a case study on interacting multiple model algorithm [J].

Mao, Yanhui ;

Han, Chongzhao ;

Duan, Zhansheng .

IET SIGNAL PROCESSING, 2014, 8 (02) :202-210

← 1 2 3 →