Modeling of non-linear dynamic systems via discrete-time recurrent neural networkks and variational training algorithm

被引：0

作者：

Minchev, SV ^{[1
]}

Venkov, GI ^{[1
]}

机构：

[1] Tech Univ Sofia, Fac Appl Math & Informat, Sofia, Bulgaria

来源：

2004 2ND INTERNATIONAL IEEE CONFERENCE INTELLIGENT SYSTEMS, VOLS 1 AND 2, PROCEEDINGS | 2004年

关键词：

current transformer; learning algorithm; hysteresis; recurrent neural networks; non-linear system;

D O I：

10.1109/IS.2004.1344645

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a discrete-time recurrent neural network architecture and parameter adaptation algorithm for modeling of non-linear dynamic systems. The learning algorithm is based on variational calculus and operates off-line. A neural network based current transformer non-linear model is presented as a demonstration of the proposed architecture and learning algorithm. It is designed for power engineering needs in power systems and is suited for real-time applications in digital relay protections.

引用

页码：105 / 108

页数：4

共 14 条

[1]

[Anonymous], 1990, IEEE T NEURAL NETWOR

[2] ON THE APPROXIMATE REALIZATION OF CONTINUOUS-MAPPINGS BY NEURAL NETWORKS [J].

FUNAHASHI, K .

NEURAL NETWORKS, 1989, 2 (03) :183-192

[3] Model-based recurrent neural network for modeling nonlinear dynamic systems [J].

Gan, CY ;

Danai, K .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2000, 30 (02) :344-351

[4] Nonlinear system identification using additive dynamic neural networks -: Two on-line approaches [J].

Griñó, R ;

Cembrano, G ;

Torras, C .

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS, 2000, 47 (02) :150-165

[5] THEORY OF FERROMAGNETIC HYSTERESIS [J].

JILES, DC ;

ATHERTON, DL .

JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS, 1986, 61 (1-2) :48-60

[6] APPROXIMATION OF DISCRETE-TIME STATE-SPACE TRAJECTORIES USING DYNAMIC RECURRENT NEURAL NETWORKS [J].

JIN, L ;

NIKIFORUK, PN ;

GUPTA, MM .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1995, 40 (07) :1266-1270

[7] Recurrent neural networks for phasor detection and adaptive identification in power system control and protection [J].

Kamwa, I ;

Grondin, R ;

Sood, VK ;

Gagnon, C ;

Nguyen, VT ;

Mereb, J .

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 1996, 45 (02) :657-664

[8]

MILLER WT, 1990, NEURAL NETWORKS CONT

[9]

MINCHEV S, P 13 INT S SIELA 200, P158

[10] APPLICATION OF THE RECURRENT MULTILAYER PERCEPTRON IN MODELING COMPLEX PROCESS DYNAMICS [J].

PARLOS, AG ;

CHONG, KT ;

ATIYA, AF .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1994, 5 (02) :255-266

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