Details

Title

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

Journal title

Archives of Control Sciences

Yearbook

2016

Issue

No 1

Authors

Divisions of PAS

Nauki Techniczne

Publisher

Committee of Automatic Control and Robotics PAS

Date

2016

Identifier

DOI: 10.1515/acsc-2016-0007 ; ISSN 1230-2384

Source

Archives of Control Sciences; 2016; No 1

References

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
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