Two- and Three-Layer Recurrent Elman Neural Networks as Models of Dynamic Processes
Antoni Wysocki , Maciej Ławryńczuk
AbstractThe goal of paper is to study and compare the effectiveness of two- and three-layer Elman recurrent neural networks used for modelling of dynamic processes. Training of such networks is discussed. For a neutralisation reactor benchmark system it is shown that the rudimentary Elman structure with two layers is much better in terms of accuracy and the number of parameters. Furthermore, its training is much easier.
|Publication size in sheets||0.5|
|Book||Szewczyk Roman, Kaliczyńska Małgorzata, Zieliński Cezary: Challenges in Automation, Robotics and Measurement Techniques. Proceedings of AUTOMATION-2016, March 2-4, 2016, Warsaw, Poland, Advances in Intelligent Systems and Computing, vol. 440, 2016, Springer International Publishing, ISBN 978-3-319-29356-1, [978-3-319-29357-8], 919 p., DOI:10.1007/978-3-319-29357-8|
|project||Development of methodology of control, decision support and production management. Project leader: Zieliński Cezary,
, Phone: 5102, start date 19-05-2015, end date 31-12-2016, 504/02233/1031, Completed
|Score|| = 15.0, 27-03-2017, BookChapterSeriesAndMatConf|
= 15.0, 27-03-2017, BookChapterSeriesAndMatConf
|Citation count*||0 (2018-06-16)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.