Version 1
: Received: 27 September 2020 / Approved: 28 September 2020 / Online: 28 September 2020 (14:12:12 CEST)
How to cite:
Matviychuk, Y.; Peráček, T.; Shakhovska, N. A Math Approach with Brief Cases towards Reducing Computational and Time Complexity in the Industrial Systems. Preprints2020, 2020090687. https://doi.org/10.20944/preprints202009.0687.v1
Matviychuk, Y.; Peráček, T.; Shakhovska, N. A Math Approach with Brief Cases towards Reducing Computational and Time Complexity in the Industrial Systems. Preprints 2020, 2020090687. https://doi.org/10.20944/preprints202009.0687.v1
Matviychuk, Y.; Peráček, T.; Shakhovska, N. A Math Approach with Brief Cases towards Reducing Computational and Time Complexity in the Industrial Systems. Preprints2020, 2020090687. https://doi.org/10.20944/preprints202009.0687.v1
APA Style
Matviychuk, Y., Peráček, T., & Shakhovska, N. (2020). A Math Approach with Brief Cases towards Reducing Computational and Time Complexity in the Industrial Systems. Preprints. https://doi.org/10.20944/preprints202009.0687.v1
Chicago/Turabian Style
Matviychuk, Y., Tomáš Peráček and Natalya Shakhovska. 2020 "A Math Approach with Brief Cases towards Reducing Computational and Time Complexity in the Industrial Systems" Preprints. https://doi.org/10.20944/preprints202009.0687.v1
Abstract
The paper proposes a new principle of finding and removing elements of mathematical model, redundant in terms of parametric identification of the model. It allows reducing computational and time complexity of the applications built on the model. Especially this is important for AI based systems, systems based on IoT solutions, distributed systems etc. Besides, the complexity reduction allows increasing an accuracy of mathematical models implemented. Despite the model order reduction methods are well known, they are extremely depended however on the problem area. Thus, proposed reduction principles can be used in different areas, what is demonstrated in this paper. The proposed method for the reduction of mathematical models of dynamic systems allows also the assessment of the requirements for the parameters of the simulator elements to ensure the specified accuracy of dynamic similarity. Efficiency of the principle is shown on the ordinary differential equations and on the neural network model. The given examples demonstrate efficient normalizing properties of the reduction principle for the mathematical models in the form of neural networks.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
