Delay probability in adaptive systems based on activation function of classical neural networks

Many improved algorithms have been proposed for nonlinear system designs. There is no single procedure for providing an algorithm with closed-form system response relations as a function of system parameters. In this paper, we illustrate a unique method for discrete-time digital nonlinear systems. Provides better insight into the analyzed system, algorithm, and processes. The main contribution is closed-form symbolic responses in the time domain and modifications of the implemented algorithm. A comparison of adaptive systems and neural networks is also presented. The design and analysis of nonlinear systems are more clearly simplified for either engineers or researchers without deep mathematical knowledge.

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Publication Details

Journal
Egyptian Informatics Journal
Published
2024-10-14
DOI
https://doi.org/10.1016/j.eij.2024.100555
Citations
1
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.35
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article

Delay probability in adaptive systems based on activation function of classical neural networks

Danijela Milošević, Vladimir Orlić, Vladimir Mladenović, Asutosh Kar et al.
1 citations
Egyptian Informatics Journal
Neural Networks and Applications
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article

Delay probability in adaptive systems based on activation function of classical neural networks

Danijela Milošević, Vladimir Orlić, Vladimir Mladenović, Asutosh Kar, Maja Lutovac
article en
1 citations

Abstract

Many improved algorithms have been proposed for nonlinear system designs. There is no single procedure for providing an algorithm with closed-form system response relations as a function of system parameters. In this paper, we illustrate a unique method for discrete-time digital nonlinear systems. Provides better insight into the analyzed system, algorithm, and processes. The main contribution is closed-form symbolic responses in the time domain and modifications of the implemented algorithm. A comparison of adaptive systems and neural networks is also presented. The design and analysis of nonlinear systems are more clearly simplified for either engineers or researchers without deep mathematical knowledge.

Egyptian Informatics JournalVol. 28
University of Kragujevac (RS), RT-RK Institute for Computer Based Systems (Serbia) (RS), Institute of Public Health of Serbia (RS), Dr. B. R. Ambedkar National Institute of Technology Jalandhar (IN)
Openalex Percentile: Top 33%
Neural Networks and Applications
0.35
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