Cite

[1] L. B. Almeida, Backpropagation in non-feedforward networks, Proc. of the NATO ARW on Neural Computers, Springer Verlag, Heidelberg, 74–91, 1993.Search in Google Scholar

[2] J. Bilski, B. Kowalczyk, A. Marchlewska, J.M. Zurada, Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks, Journal of Artificial Intelligence and Soft Computing Research, 10(4), 2020.10.2478/jaiscr-2020-0020Search in Google Scholar

[3] T. Chmielniak, P. Krzys´lak, Comparative analysis of energy potential of three ways of configuration of a condenser power plant thermal cycle, Polish Marit. Res., 15, 30–36, 2008.10.2478/v10012-007-0094-xSearch in Google Scholar

[4] T. Chmielniak, H. Łukowicz, Condensing power plant cycle-Assessing possibilities of improving its efficiency, Arch. Thermodyn., 31, 105–113, 2010.10.2478/v10173-010-0017-6Search in Google Scholar

[5] K. Cpałka, Design of interpretable fuzzy systems, Springer, 2017.10.1007/978-3-319-52881-6Search in Google Scholar

[6] P. Deshpande, N. Warke, P. Khandare, V. Deshpande, Thermal power plant analysis using artificial neural network, Proc. of the 3rd Nirma Univ. Int. Conf. Eng. NUiCONE 2012, 1–6, 2012.10.1109/NUICONE.2012.6493290Search in Google Scholar

[7] R. Dixit, J. Kumar, T. Soota, Modeling of a Thermal Power Plant using Neural Network and Regression Modeling of a Thermal Power Plant using Neural Network and Regression Technique, 2018.Search in Google Scholar

[8] P. Duda, M. Jaworski, A. Cader, L. Wang, On Training Deep Neural Networks Using a Streaming Approach Journal of Artificial Intelligence and Soft Computing Research, 10(1), pp. 15–26, 2020.10.2478/jaiscr-2020-0002Search in Google Scholar

[9] A. Gardzilewicz, J. Głuch, M. Bogulicz, R. Walkowiak, M. Najwer, J. Kiebdoj, Experience in Application of Thermal Diagnostics in the Turow Power Station, Proc. of the Int. Joint Power Generation Conference collocated with TurboExpo 2003 (2003 Int. Joint Power Generation Conference, Atlanta, Georgia, USA), 371–378, 2003.10.1115/IJPGC2003-40017Search in Google Scholar

[10] J. C. Gallagher, S. K. Boddhu, S. Vigraham, A reconfigurable continuous time recurrent neural network for evolvable hardware applications, In Proc. of the 2005 IEEE Congress on Evolutionary Computation, Piscataway, NJ: IEEE, 2005.Search in Google Scholar

[11] J. Głuch, Fault detection in measuring systems of power plants, Polish Marit. Res., 15, 45–51, 2008.10.2478/v10012-007-0096-8Search in Google Scholar

[12] Y. Guo, Y. Liu, T. Georgiou, M. S. Lew, A review of semantic segmentation using deep neural networks, Int. Journal of Multimedia Information Retrieval, 2, 1–7, 2017.10.1007/s13735-017-0141-zSearch in Google Scholar

[13] R. Isermann, Supervision FDD Methods-An Introduction, Control Eng. Pract., 5, 639–652, 1997.10.1016/S0967-0661(97)00046-4Search in Google Scholar

[14] F. B. Ismail Alnaimi, H. H. Al-Kayiem, Artificial intelligent system for steam boiler diagnosis based on superheater monitoring, J. Appl. Sci., 11, 2011.10.3923/jas.2011.1566.1572Search in Google Scholar

[15] A. M. Kler, P. V. Zharkov, N. O. Epishkin, Parametric optimization of supercritical power plants using gradient methods, Energy, 189, 2019.10.1016/j.energy.2019.116230Search in Google Scholar

[16] K. Kosowski, Introduction to the Theory of Marine Turbines, Foundation for the Promotion of Maritime Industry, Gdańsk, 2005.Search in Google Scholar

[17] K. Kosowski, Ship Turbine Power Plants, Fundamentals of Thermodynamical Cycles, Foundation for the Promotion of Maritime Industry, Gdańsk, 2005.Search in Google Scholar

[18] Ł. Kowalczyk, W. Elsner, P. Niegodajew, M. Marek, Gradient-free methods applied to optimisation of advanced ultra-supercritical power plant, Appl. Therm. Eng., 96, 200–208, 2016.10.1016/j.applthermaleng.2015.11.091Search in Google Scholar

[19] J. K Mohanty., A. Adarsh, P. R. Dash, K. Parida, P. K. Pradhan, Integrated Condition Monitoring of Large Captive Power Plants and Aluminum Smelters, Sound&Vibration., 53, 223–235, 2019.10.32604/sv.2019.07737Search in Google Scholar

[20] T. Niksa-Rynkiewicz, A. Witkowska, Analisis of impact of ship model parameters on changes of control quality index in ship dynamic positioning system, Polish Maritime Research, 2019.10.2478/pomr-2019-0001Search in Google Scholar

[21] S. Osowski, Signal flow graphs and neural networks. Biological Cybernetics, 70, 387–395, 1994.10.1007/BF00200336Search in Google Scholar

[22] F. J. Pineda, Generalization of Back-Propagation to Recurrent Neural Network, The American Physical Society, 59(19), 1988.10.1103/PhysRevLett.59.222910035458Search in Google Scholar

[23] F. J. Pineda, Recurrent Back-Propagation and the Dynamical Approach to Adaptive Neural Computation, Neural Computation, 1(2), 161–172, 1989.10.1162/neco.1989.1.2.161Search in Google Scholar

[24] M. A. Ranzato, A. Szlam, J. Bruna, M. Mathieu, R. Collobert, S. Chopra, Video (language) modeling: A baseline for generative models of natural videos. arXiv:1412.6604, 2014.Search in Google Scholar

[25] W. Rawat, Z. Wang, Deep convolutional neural Networks for image classification: A comprehensive review, Neural Computation, 29(9), 1–10, 2017.10.1162/neco_a_00990Search in Google Scholar

[26] A. J. Robinson, F. Fallside, The utility driven dynamic error propagation network. Cambridge: University of Cambridge Department of Engineering, 1987.Search in Google Scholar

[27] L. Rutkowski, Computational intelligence: methods and techniques, Springer Science & Business Media, 2008.Search in Google Scholar

[28] A. Saeed A. Rashid, Development of Core Monitoring System for a Nuclear Power Plant using Artificial Neural Network Technique, Annals of Nuclear Energy, 144(1), 2020.10.1016/j.anucene.2020.107513Search in Google Scholar

[29] M. Saghafia, M. B. Ghofranib, Real-time estimation of break sizes during LOCA in nuclear power plants using NARX neural network, Nuclear Engineering and Technology 51(3), 702–708, 2019.10.1016/j.net.2018.11.017Search in Google Scholar

[30] T. K. Sai, K. A. Reddy, Neural Network Applications in a Power Station, Int. J. Soft Comput., 6, 01–16, 2015.10.5121/ijsc.2015.6201Search in Google Scholar

[31] P. Sharma, A. Singh, Era of deep neural networks: A review, In Proc. of the 8th Int. Conf. on Computing, Communication and Networking Technologies, Piscataway, NJ: IEEE, 1–5, 2017.Search in Google Scholar

[32] J. Smrekar, D. Pandit, M. Fast, M. Assadi, D. Sudipta, Prediction of power output of a coal-fired power plant by artificial neural network, Neural Comput. Appl., 19, 725–740, 2010.10.1007/s00521-009-0331-6Search in Google Scholar

[33] A. Witkowska., T. Niksa-Rynkiewicz, Dynamically positioned ship steering making use of backstepping method and artificial neural networks, Polish Maritime Research, 2018.10.2478/pomr-2018-0126Search in Google Scholar

[34] P. J. Werbos, Generalization of backpropagation with application to a recurrent gas market model. Neural Networks, 1(4), 339–356, 1988.10.1016/0893-6080(88)90007-XSearch in Google Scholar

[35] G. Yang Y. Wang, X. Li, Prediction of the NOx emissions from thermal power plant using long-short term memory neural network, Energy, 192(1), 2020.10.1016/j.energy.2019.116597Search in Google Scholar

[36] Y. Yu, X. Si, C. Hu, J. Zhang, A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures, Neural Computation, 31(7), 1235–1270, 2019.10.1162/neco_a_0119931113301Search in Google Scholar

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