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References (selected)

... 2013

[1] Bukovsky, I. : Modeling of Complex Dynamic Systems by Nonconventional Artificial Neural Architectures and Adaptive Approach to Evaluation of Chaotic Time Series, Ph.D. THESIS, Faculty of Mechanical Engineering, Czech Technical University in Prague, 2007.

[2] Bukovsky, I., Bila. J: “ Adaptive Evaluation of Complex Dynamic Systems using Low-Dimensional Neural Architectures”, in Advances in Cognitive Informatics and Cognitive Computing, Series: Studies in Computational Intelligence, Vol. 323/2010, eds. D. Zhang, Y. Wang, W. Kinsner, Springer-Verlag Berlin Heidelberg, 2010, ISBN: 978-3-642-16082-0, pp.33-57.

[3] Bukovsky, I., Kinsner, W., Bila, J.: „ Multiscale Analysis Approach for Novelty Detection in Adaptation Plot“, 3rd Sensor Signal Processing for Defence 2012 (SSPD 2012), Imperial College London, UK, September 24-27, 2012, doi: 10.1049/ic.2012.0114, E-ISBN: 978-1-84919-712-0.

[4] Bukovsky, I.: "Learning Entropy: Multiscale Measure for Incremental Learning", Entropy 2013, 15, 4159-4187. (Full text pdf or via: http://www.mdpi.com/1099-4300/15/10/4159 ).

2014

[5] P. M. Benes et al., "Neural Network Approach to Railway Stand Lateral Skew Control" in Computer Science & Information Technology (CS& IT), Sydney, NSW, Australia, AIRCC, 2014, pp. 327-339

[6] M. Cejnek, P. M. Benes, and I. Bukovsky, “Another Adaptive Approach to Novelty Detection in Time Series,” in Computer Science & Information Technology (CS& IT), Sydney, NSW, Australia, AIRCC, 2014pp. 341–351.

[7] Peter Benes and Ivo Bukovsky: "Neural Network Approach to Hoist Deceleration Control", The 2014 International Joint Conference on Neural Networks (IJCNN 2014), IEEE WCCI 2014, Beijing, 2014.

[8] Ivo Bukovsky et al.,:" Learning Entropy for Novelty Detection: A Cognitive Approach for Adaptive Filters",  Sensor Signal Processing for Defence (SSPD) Conference 2014 , Edinburgh, UK, Sept. 8-9, 2014

[9] Ivo Bukovsky, Noriyasu Homma, Matous Cejnek and Kei Ichiji: "Study of Learning Entropy for Novelty Detection in Lung Tumor Motion Prediction for Target Tracking Radiation Therapy" , The 2014 International Joint Conference on Neural Networks (IJCNN 2014), IEEE WCCI 2014, Beijing, 2014.

2015

[10] Bukovsky, I., Homma, N., et al: "A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications", BioMed Research International, issue Radiation Oncology and Medical Physics (ROMP), vol. 2015, Article ID 489679, 13 pages, 2015. doi:10.1155/2015/489679.

[11] Bukovsky, I., Benes, P.:"Adaptive Control of Laboratory Systems with Higher Order Neural Units", CSOC 2015, Springer Series: Advances in Intelligent Systems and Computing, 2015, ISSN 2194-5357

[12] Bukovsky, I., Oswald, C.:"Case Study of Learning Entropy for Adaptive Novelty Detection in Solid-fuel Combustion Control", CSOC 2015, Springer Series: Advances in Intelligent Systems and Computing, 2015, ISSN 2194-5357

[13] Bukovsky, I., Vesely, M., Benes, P., M., Erben, M.:“Adaptivní polynomiální regulátor s více stupni volnosti,” in Automatizácia a riadenie v teórii a praxi 2015, Slovensko, 2015, pp. 50–1 – 50–10.

[14] Cejnek, M., Bukovsky, I.: “Adaptivní prístup k detekci demence z EEG,” in Automatizácia a riadenie v teórii a praxi 2015, Slovensko, 2105, pp. 51–1 – 51–8.

[15] Vesely, M., Bukovsky, I., Vrba, J.:"Adaptivní gradientní systém s inverzním referencním modelem (AGSIRM)", in Automatizácia a riadenie v teórii a praxi 2015, Slovensko, 2105, pp. 52–1 – 52–8.

[16] Erben, M., Bukovsky, I.:"Adaptivní referencní detekce zmen chování soustav v regulacním obvodu", in Automatizácia a riadenie v teórii a praxi 2015, Slovensko, 2105, pp. 53–1 – 53–8.

[17] Bukovsky, I., Oswald, C., Vrba, J.: “Prípadová studie použití entropie ucení pro adaptivní detekci pri rízení spalování tuhých paliv,” in Automatizácia a riadenie v teórii a praxi 2015, Slovensko, 2015, pp. 68–1 – 18–11. 

[18] M. Cejnek, I. Bukovsky, N. Homma, and O. Liska, “ Adaptive Classification of EEG for Dementia Diagnosis,”  International Workshop on Computational Intelligence for Multimedia Understanding, IEEE, Prague, Czech Republic, 2015.

[19] M. Cejnek, I. Bukovsky, and O. Vysata, “Adaptive Polynomial Filters with Individual Learning Rates For Computationally Efficient Lung Tumor Motion Prediction,” presented at the International Workshop on Computational Intelligence for Multimedia Understanding, IEEE,  Prague, Czech Republic, 2015.

[20] X. Zhang, N. Homma, K. Ichiji, M. Abe, N. Sugita, I. Bukovsky, Y. Takai, and M. Yoshizawa, “ Tumor motion tracking using kV/MV X-ray fluoroscopy for adaptive radiation therapy,” in 2015 International Workshop on Computational Intelligence for Multimedia Understanding IWCIM), 2015, pp. 1–4.

2016

[21] P. Benes and I. Bukovsky, “On the Intrinsic Relation between Linear Dynamical Systems and Higher Order Neural Units,” in Intelligent Systems in Cybernetics and Automation Theory, R. Silhavy, R. Senkerik, Z. K. Oplatkova, Z. Prokopova, and P. Silhavy, Eds. Springer International Publishing, 2016.

[22] Peter Mark Benes, Miroslav Erben, Martin Vesely, Ondrej Liska, and Ivo Bukovsky, “HONU and Supervised Learning Algorithms in Adaptive Feedback Control,” in Applied Artificial Higher Order Neural Networks for Control and Recognition, IGI Global, 2016.

[23] Cyril Oswald, Matous Cejnek, Jan Vrba, and Ivo Bukovsky, “Novelty Detection in System Monitoring and Control with HONU,” in Applied Artificial Higher Order Neural Networks for Control and Recognition, IGI Global, 2016.

[24] Ivo Bukovsky, Matous Cejnek, Jan Vrba, Noriyasu Homma. “Study of Learning Entropy for Onset Detection of Epileptic Seizures in EEG Time Series”, The 2016 International Joint Conference on Neural Networks (IJCNN 2016), IEEE WCCI 2016, Vancouver, 2016 (accepted paper 01/2016)

[25]  Matous Cejnek, Ivo Bukovsky, “Online Data Centering Modifications for Adaptive Filtering with NLMS Algorithm”, The 2016 International Joint Conference on Neural Networks (IJCNN 2016), IEEE WCCI 2016, Vancouver, 2016 (accepted paper 01/2016)

[26] Ivo Bukovsky, Peter Benes, Martin Vesely, Jan Pitel, Madan M. Gupta, “Model Reference Multiple-Degree-of-Freedom Adaptive Control with HONUs”, The 2016 International Joint Conference on Neural Networks (IJCNN 2016), IEEE WCCI 2016, Vancouver, 2016 (accepted paper 01/2016)

[27] Josef Bostik, Martin Klimt, Matej Mojzes, Jaromir Kukal, Ivo Bukovsky and Matous Cejnek, “V-Shaped Neurons in Hidden Layer of ANN Universal Approximator without Flat Domains”, The 2016 International Joint Conference on Neural Networks (IJCNN 2016), IEEE WCCI 2016, Vancouver, 2016 (accepted paper 01/2016)

[28] Matej Mojzes, Martin Klimt, Jaromir Kukal, Ivo Bukovsky, Jan Vrba and Jan Pitel, “Feature Selection via Competitive Levy Flights”, The 2016 International Joint Conference on Neural Networks (IJCNN 2016), IEEE WCCI 2016, Vancouver, 2016 (accepted paper 01/2016)

[29] Cyril Oswald, “Adaptivní detekce nových událostí v měřených datech pomocí kombinace SOM a HONU”, in Automatizácia a riadenie v teórii a praxi 2016, Slovensko, 2016.

[30] Martin Veselý, Ivo Bukovský, “Adaptivní neuroregulátor se sériově-paralelním referenčním modelem”, in Automatizácia a riadenie v teórii a praxi 2016, Slovensko, 2016.

[31] Viktor Plaček, Ivo Bukovský, Cyril Oswald, “Měření rychlosti postupu svarové lázně”, in Automatizácia a riadenie v teórii a praxi 2016, Slovensko, 2016.

[32]  Ivo Bukovsky, Noriyasu Homma, "An Approach to Stable Gradient Descent Adaptation of Higher-Order Neural Units", IEEE Transactions on Neural Networks and Learning Systems , 2016, DOI:10.1109/TNNLS.2016.2572310.