Electromagnetic Compatibility Estimator Using Scaled Conjugate Gradient Backpropagation Based Artificial Neural Network

被引：28
|
作者：
Khadse, Chetan B. [1 ,2 ]
Chaudhari, Madhuri A. [3 ]
Borghate, Vijay B. [3 ]
机构：
[1] Dhamangaon Educ Societys Coll Engn & Technol, Dept Elect Engn, Amravati 444709, India
[2] Visvesvaraya Natl Inst Technol, Nagpur 440010, Maharashtra, India
[3] Visvesvaraya Natl Inst Technol, Dept Elect, Nagpur 440010, Maharashtra, India
来源：
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2017年 / 13卷 / 03期
关键词：
Data acquisition; electromagnetic compatibility; neural network; real-time system; signal processing; DIMENSIONALITY REDUCTION; FEATURE-EXTRACTION; VOLTAGE DIPS; S-TRANSFORM; CLASSIFICATION; MACHINE; ALGORITHMS; FUZZY;
D O I：
10.1109/TII.2016.2605623
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, an electromagnetic compatibility estimator is proposed using an artificial neural network with a scaled conjugate gradient algorithm. Neural networks are trained with the help of seven different optimization algorithms in MATLAB. Their performance is evaluated on the basis of number of neurons, desired output, and mean-squared error in offline mode in MATLAB. Among seven algorithms, scaled conjugate gradient algorithm is found to be the best choice. Hence, it is implemented in LabVIEW for online assessment of electromagnetic compatibility issues. Voltage dip, swell, and harmonics are generated with the help of an experimental setup. It consists of 230 V, 50 Hz input voltage supply, microcontroller, variac, and solid-state relays. It is interfaced to the LabVIEW software with the help of an NI USB 6361 data acquisition system. It enabled the continuous online monitoring of various signals. Along with voltage dip and swell, harmonics are also evaluated with the help of spectrum analyzer in LabVIEW. The detailed description of a hardware setup and mathematical modeling of trained network is given in this paper.
引用
收藏
页码：1036 / 1045
页数：10
相关论文
共 50 条
  • [31] Gradient Descent with Momentum Based Backpropagation Neural Network for Selection of Industrial Robot
    Nayak, Sasmita
    Choudhury, B. B.
    Lenka, Saroj Kumar
    [J]. PROCEEDINGS OF FIRST INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY FOR INTELLIGENT SYSTEMS: VOL 1, 2016, 50 : 487 - 496
  • [32] A modified conjugate gradient-based Elman neural network
    Li, Long
    Xie, Xuetao
    Gao, Tao
    Wang, Jian
    [J]. COGNITIVE SYSTEMS RESEARCH, 2021, 68 : 62 - 72
  • [33] Botnet Detection Using a Feed-Forward Backpropagation Artificial Neural Network
    Ahmed, Abdulghani Ali
    [J]. COMPUTATIONAL INTELLIGENCE IN INFORMATION SYSTEMS (CIIS 2018), 2019, 888 : 24 - 35
  • [34] Design of SIBI Sign Language Recognition Using Artificial Neural Network Backpropagation
    Handayani, Anik Nur
    Akbar, Muhammad Iqbal
    Ar-Rosyid, Harits
    Ilham, Muhammad
    Asmara, Rosa Andrie
    Fukuda, Osamu
    [J]. 2022 2ND INTERNATIONAL CONFERENCE ON INTELLIGENT CYBERNETICS TECHNOLOGY & APPLICATIONS (ICICYTA), 2022, : 192 - 197
  • [35] Training of an Artificial Neural Network with Backpropagation Algorithm Using Notification Oriented Paradigm
    Schutz, Fenando
    Fabro, Joao A.
    Lima, Carlos R. E.
    Ronszcka, Adriano F.
    Stadzisz, Paulo C.
    Simao, Jean M.
    [J]. 2015 LATIN AMERICA CONGRESS ON COMPUTATIONAL INTELLIGENCE (LA-CCI), 2015,
  • [36] Analysis Resilient Algorithm on Artificial Neural Network Backpropagation
    Saputra, Widodo
    Tulus
    Zarlis, Muhammad
    Sembiring, Rahmat Widia
    Hartama, Dedy
    [J]. INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY (ICONICT), 2017, 930
  • [37] Prediction of inelastic displacement ratios for evaluation of degrading SDOF systems: A comparison of the scaled conjugate gradient and Bayesian regularized artificial neural network modeling
    Borekci, Muzaffer
    Aydogan, Burak
    [J]. SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI, 2024, 42 (01): : 211 - 224
  • [38] Classification of Locally Grown Apple Based On Its Decent Consuming Using Backpropagation Artificial Neural Network
    Muladi, Muladi
    Lestari, Dyah
    Prasetyo, Dedy Tri
    Wibawa, Aji Prasetya
    Widiyaningtyas, Triyanna
    Pujianto, Utomo
    [J]. 2019 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS AND INFORMATION ENGINEERING (ICEEIE), 2019, : 96 - 100
  • [39] Topographic property of backpropagation artificial neural network: From human functional connectivity network to artificial neural network
    Chen, Heng
    Lu, Fengmei
    He, Bifang
    [J]. NEUROCOMPUTING, 2020, 418 (418) : 200 - 210
  • [40] Artificial neural network-based peak load forecasting using conjugate gradient methods (vol 17, pg 907, 2002
    Saini, LM
    Soni, MK
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2002, 17 (04) : 1290 - 1290
12345