Szczegóły

Tytuł artykułu

Elman neural network for modeling and predictive control of delayed dynamic systems

Tytuł czasopisma

Archives of Control Sciences

Rocznik

2016

Numer

No 1

Autorzy publikacji

Wydział PAN

Nauki Techniczne

Abstrakt

<jats:p>The objective of this paper is to present a modified structure and a training algorithm of the recurrent Elman neural network which makes it possible to explicitly take into account the time-delay of the process and a Model Predictive Control (MPC) algorithm for such a network. In MPC the predicted output trajectory is repeatedly linearized on-line along the future input trajectory, which leads to a quadratic optimization problem, nonlinear optimization is not necessary. A strongly nonlinear benchmark process (a simulated neutralization reactor) is considered to show advantages of the modified Elman neural network and the discussed MPC algorithm. The modified neural model is more precise and has a lower number of parameters in comparison with the classical Elman structure. The discussed MPC algorithm with on-line linearization gives similar trajectories as MPC with nonlinear optimization repeated at each sampling instant.</jats:p>

Wydawca

Committee of Automatic Control and Robotics PAS

Data

2016

Identyfikator

ISSN 1230-2384

Referencje

WU (1996), Prediction of geomagnetic storms from solar wind data using Elman recurrent neural networks, Geophysical Research Letters, 23, 319, doi.org/10.1029/96GL00259 ; ELMAN (1990), Finding structure in time, Cognitive Science, 14, 179, doi.org/10.1207/s15516709cog1402_1 ; DECLERCQ (1996), Comparative study of neural predictors in model based predictive control In Proc of Int Workshop on Neural Networks for Identification Control Robotics and Signal, Image Processing, 20. ; MANDIC (2001), Recurrent Neural Networks for Prediction : Learning Algorithms Architectures and Stability New York, USA. ; HAGAN (1996), Neural Network Design PWS Publishing Co, USA. ; HOVORKA (2004), Nonlinear model predictive control of glucose concentration in subjects with type diabetes, Physiological Measurement, 25, 905, doi.org/10.1088/0967-3334/25/4/010 ; GÓMEZ (2004), Wiener model identification and predictive control of a ph neutralisation process Proceedings : Control Theory and Applications, IEE, 151, 329. ; VEGA (1998), ZAMARRE NO and State - space neural network properties and application, Neural Networks, 11, 1099, doi.org/10.1016/S0893-6080(98)00074-4 ; ŁAWRYŃCZUK (2011), Accuracy and computational efficiency of suboptimal nonlinear predictive control based on neural models, Applied Soft Computing, 11, 2202, doi.org/10.1016/j.asoc.2010.07.021 ; PLAWIAK (2014), Approximation of phenol concentration using novel hybrid computational intelligence methods of Applied Mathematics and Computer, Science, 24, 165. ; LI (2014), Prediction of urban rail transit sectional passenger flow based on elman neural network and Materials, Applied Mechanics, 505. ; HAYKIN (1998), Neural Networks : A Comprehensive Foundation PTR Upper Saddle River nd edition, USA, 2. ; QIN (2003), A survey of industrial model predictive control technology, Control Engineering Practice, 11, 733, doi.org/10.1016/S0967-0661(02)00186-7 ; BONNEAU (2007), A predictive model for transcriptional control of physiology in a free living cell, Cell, 131, 1354, doi.org/10.1016/j.cell.2007.10.053

DOI

10.1515/acsc-2016-0007

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