Controlling Digital Systems via Intelligent Networks

Authors

  • Ghadeer Ibrahim Maki University of Kufa
  • Zahir M. Hussain University of Kufa

DOI:

https://doi.org/10.31642/JoKMC/2018/080105

Keywords:

Digital control system, Artificial Neural Network, Deep learning

Abstract

: Control is important to improve hardware performance. Most electronic systems are designed according to the device and then manufactured as an attached electronic device. However, if conditions change or the factory is modernized then the control device must be replaced. This is due to the complexity of the control unit represented by the program implementation algorithms, in addition to the time delay caused by digital and analog signal converters (ADC - DAC), and in this research it is replaced by deep neural networks It is a thriving field with practical and medical applications and is characterized by its ability to learn and train as it is a branch of machine learning and artificial intelligence. The results proved that the functioning of the neural networks and their performance are better than the control system where the value of the difference between the two is equal to zero.

Downloads

Download data is not yet available.

References

Ms. Sami Fadali and Antonio Visioli, Digital control engineering: analysis and design, Academic Press, 20.

Jacob. M. Williams, "Deep learning and transfer learning in the classification of eeg signals," 2017.

Alexey Kornaev, Roman Zaretsky and Sergy Egorov, "Simulation of Deep Learning Control Systems to Reduce Energy Loses due to Vibration and Friction in Rotor Bearings," in 2019 3rd School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR), 2019. DOI: https://doi.org/10.1109/DCNAIR.2019.8875575

Song Xu, Seiji Hashimoto, Yuqi Jiang, Katsutoshi Izaki, Takeshi Kihara, Ryota Ikeda and Wei Jiang, "A Reference-Model-Based Artificial Neural Network Approach for a Temperature Control System," Processes, vol. 8, p. 50, 2020. DOI: https://doi.org/10.3390/pr8010050

Katsuhiko Ogata, "Modern control engineering," Book Reviews, vol. 35, p. 1184, 1999. DOI: https://doi.org/10.1016/S0005-1098(99)00017-5

Jacquot, R. G. (2019). Modern digital control systems. Routledge. DOI: https://doi.org/10.1201/9780203746721

Chi-Tsong, Chen, Analog and digital control system design: transfer-function, state-space, and algebraic methods, Oxford University Press, Inc., 1995. [8] Frank Owen, Control Systems Engineering: A Practical Approach, PolyX Publishing, 2018.

I. Nagrath and M. Gopal, Text of control systems engineering (Vtu), New Age International, 2008.

Martin T. Hagan, Howard B. Demuth and Orlando D. Jes'us, "An introduction to the use of neural networks in control systems," International Journal of Robust and Nonlinear Control: IFAC-Affiliated Journal, vol. 12, pp. 959--985, 2002. DOI: https://doi.org/10.1002/rnc.727

Enzo Grossi and Massimo Buscema, "Introduction to artificial neural networks," European of gastroenterology & hepatology, vol. 19, pp. 1046--1054, 2007. DOI: https://doi.org/10.1097/MEG.0b013e3282f198a0

Gurney, K. (1997). An introduction to neural networks. CRC press . DOI: https://doi.org/10.4324/9780203451519

Eduard Petlenkov, Neural networks based identification and control of nonlinear systems: ANARX model based approach, TUT Press, 2007.

Howard Demuth and Mark Beale, Neural Network Toolbox for Use with MATLAB: User's Guide; Computation, Visualization, Programming, MathWorks Incorporated, 1998.

Chigezie Nwankpa, Winifred. Ijomah, Anthony Gachagan and Stephen Marshall, "Activation functions: Comparison of trends in practice and research for deep learning," arXiv preprint arXiv:1811.03378, 2018.

Lim, S., Bae, J. H., Eum, J. H., Lee, S., Kim, C. H., Kwon, D., ... & Lee, J. H. (2019). Adaptive learning rule for hardware-based deep neural networks using electronic synapse devices. Neural Computing and Applications, 31(11), 8101-8116. DOI: https://doi.org/10.1007/s00521-018-3659-y

Downloads

Published

2021-03-30

How to Cite

Maki, G., & Hussain, Z. (2021). Controlling Digital Systems via Intelligent Networks. Journal of Kufa for Mathematics and Computer, 8(1), 28–32. https://doi.org/10.31642/JoKMC/2018/080105

Similar Articles

1 2 3 4 5 6 7 8 > >> 

You may also start an advanced similarity search for this article.