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References [1] Altrichter M., Horváth G. et. al.: Neurális hálózatok . Hungarian Edition Panem Könyvkiadó Kft., Budapest, 2006. [2] Krizhevsky A., Hinton G.: Learning multiple layers of features from tiny images .” Master’s Thesis. University of Toronto, Toronto, Canada, 2009. [3] Nair V., Hinton G. E.: Rectified linear units improve restricted boltzmann machines . In Proc. 27th International Conference on Machine Learning, 2010. [4] Krizhevsky, A. I. Sutskever, et. al.: ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural

References [1] D. Amic, D. Beslo, B. Lucic, S. Nikolic, N. Trinajsti ć , The vertex-connectivity index revisited, J. Chem. Inf. Comput. Sci. 38 (1998) 819-822 [2] L.F. Araghi, H. Khaloozade, M.R. Arvan, Ship identification using probabilistic neural networks. In: Proceedings of the international multiconference of engineers and computer scientists, 2(2009), 18-20 [3] M. Azari, A. Iranmanesh, Generalized Zagreb index of graphs, Studia Univ. Babes-Bolyai. 56 (3) (2011) 59-70 [4] M. Ba č a, J. Horv á thov á , M. Mokri š ov á , Andrea Semani č ov á -Fe ň ov č kov á

, Engineering Applications of Artificial Intelligence 10(1): 3-14. Fuessel D. and Isermann R. (2000). Hierarchical motor diagnosis utilising structural knowledge and a self-learning neuro-fuzzy scheme, IEEE Transactions on Industrial Electronics 47(5): 1070-1077. Gertler J. (1998). Fault Detection and Diagnosis in Engineering Systems , Marcel Dekker, New York, NY. Haykin S. (1999). Neural Networks. A Comprehensive Foundation, 2nd Ed. , Prentice-Hall, Englewood Cliffs, NJ. Iserman R. (2006). Fault Diagnosis Systems. An Introduction from Fault Detection to Fault Tolerance

References [1] SPASOJEVIĆ, S. S., ŠUŠIĆ, M. Z., DUROVIĆ, Ž. M. 2012. Recognition and classification of geometric shapes using neural networks. In: 11th Symposium on Neural Network Applications in Electrical Engineering . NEUREL-2012, Faculty of Electrical Engineering, University of Belgrade, Serbia, pp. 71-76. ISBN 978-1-4673-1572-2. [2] YANG, Y., ZHENG, P., HE, H., ZHENG, T., WANG, L., HE, S. 2018. An Evaluation Method of Acceptable and Failed Spot Welding Products Based on Image Classification with Transfer Learning Technique. In: ACM International Conference

REFERENCES [1] E wert P., W olkiewicz M., Detection methods overview of induction motor eccentricity using stator current analysis , Scientific Papers of the Institute of Electrical Machines, Drives and Measurements of the Wrocław University of Technology, Studies and Research, 2015, 35, 151–160 (in Polish). [2] K owalski C.T., O rlowska -K owalska T., Application of neural networks for the induction motor faults detection , Trans. of IMCAS Mathematics and Computers in Simulation, 2003, 63(3–5), 435–448. [3] B ouzid M.B.K., C hampenois G., B ellaaj N

References [1] J. Cao, R. Li, Fixed-time synchronization of delayed memristor-based recurrent neural networks, Sci. China. Inf. Sci. 60(3) (2017) 032201. [2] Y. Huo, J. B-Liu, J. Cao, Synchronization analysis of coupled calcium oscillators based on two regular coupling schemes, Neurocomputing 165 (2015) 126-132. [3] Z. Guo, J. Wang, Z. Yan, Attractivity analysis of memristor-based cellular neural networks with time-varying delays, IEEE Trans. Neural Netw. Learn. Syst. 25 (2013) 704-717. [4] J. Devillers, A. T. Balaban, Topological Indices and Related Descriptors

References 1. M. Rahmani, and A. Ghanbari, Hybrid neural network fraction integral terminal sliding mode control of an Inchworm robot manipulator, Vol. 80, pp.117-136, 2016. 2. H. N. Nguyen, and J. Zhou, A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network, Vol. 151, pp. 996-1005, 2015. 3. W. He, and A. O. David, Neural network control of a robotic manipulator with input dead zone and output constraint, Vol. 46, No. 6, pp. 759-770, 2016. 4. M. Beyeler, and N. Oros, A GPU

References [1] R.N. Yadav, P.K. Kalra, J. John, Time series prediction with single multiplicative neuron model, Applied Soft Computing, 7, 2007, 1157-1163. [2] E. Egrioglu, C.H. Aladag, U. Yolcu, and E. Bas, Recurrent multiplicative neuron model artificial neural network for non-linear time series forecasting, Neural Processing Letters 41(2), 2015, 249-258. [3] O. Gundogdu, E. Egrioglu, C.H. Aladag, and U. Yolcu, Multiplicative neuron model artificial neural network based on gauss activation function, Neural Computing and Applications 27(4), 2015, 927-935 [4] D

-388. Sargolzaei, S., Faez, K., Sargolzaei, A. (2008). Signal processing based for fetal electrocardiogram extraction. In Proceedings of International Conference on Biomedical Engineering and Informatics , 27-30 May 2008, Vol. 2, 492-496. Golzan, S. M., Hakimpour, F., Mikaili, M., Toolou, A. (2008). Fetal ECG extraction using multi-layer perception neural networks with Bayesian approach. In Proceedings of 4 th European Conference of the International Federation for Medical and Biological Engineering (ECIFMBE) , Antwerp, Belgium, 27-30 May 2008, 1378-1385. Noguchi, Y

R eferences [1] R. T. Schirrmeister, J. T. Springenberg, …, T. Ball (2018): Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG. arXiv:1703.05051v5 [2] Nijboer, F., Sellers, E. W., …, Kübler, A. (2008): A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clincical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 119(8):1909–1916. [3] Munßinger, J. I., Halder, …, Kubler, A. (2010). Brain Painting: First