System Identification Using Artificial Neural Network

被引：0

作者：

Wilfred, K. J. Nidhil ^{[1
]}

Sreeraj, S. ^{[1
]}

Vijay, B. ^{[1
]}

Bagyaveereswaran, V. ^{[1
]}

机构：

[1] VIT Univ, SELECT, Vellore, Tamil Nadu, India

来源：

2015 INTERNATIONAL CONFERENCED ON CIRCUITS, POWER AND COMPUTING TECHNOLOGIES (ICCPCT-2015) | 2015年

关键词：

system identification; neural network; step response; nonlinear identification; initial condition; ROBUST IDENTIFICATION; DEAD-TIME;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

System identification is one of the important aspects that needed to be considered before the controller design. The main objective of system identification is to know the model of the system. It is essential to understand the process before handling it. Then we can go for controller design which is apt for the system. A number of methods are existing for system identification. In this paper we propose a method to identify the system model. The proposed method involves use of back propagation neural network to predict the output of the system for a given input from the knowledge of past inputs & outputs. The effectiveness of the model identification is tested using experimental data from pressure process station, level process station, and conical tank process.

引用

页数：4

共 12 条

[1] Bayaveereswaran V., 2013, INT J APPL ENG RES, V8, P2165
[2] Robust identification of first-order plus dead-time model from step response
Bi, Q
Cai, WJ
Lee, EL
Wang, QG
Hang, CC
Zhang, Y
[J]. CONTROL ENGINEERING PRACTICE, 1999, 7 (01) : 71 - 77
[3] A NEW METHOD FOR DETERMINING PARAMETERS OF PHYSICAL SYSTEMS
DIAMESSIS, JE
[J]. PROCEEDINGS OF THE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS, 1965, 53 (02): : 205 - +
[4] FACCHINI L, 2014, ARTICLE COMPUTERS ST, V138, P183, DOI DOI 10.1016/J.COMPSTRUC.2014.01.013
[5] Lian J., 2008, IEEE T NEURAL NETWOR, V19
[6] Liu G. P., 1999, IEEE T T SYST MAN B, V398, P34
[7] Mekki H., VARIABLE STRUC UNPUB
[8] Variable structure neural networks for adaptive control of nonlinear systems using the stochastic approximation
Mekki, Hassen
Chtourou, Mohamed
Derbel, Nabil
[J]. SIMULATION MODELLING PRACTICE AND THEORY, 2006, 14 (07) : 1000 - 1009
[9] Nicoletti M. D. C., 2007, INT J INNOV COMPUT I, V1, P1
[10] Nicoletti MD, 2009, STUD COMPUT INTELL, V258, P1

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