<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In recent years, we are witnessing artificial intelligence being deployed on embedded platforms in our everyday life, including engineering design practice problems starting from early stage design ideas to the final decision. One of the most challenging problems is related to the design and implementation of neural networks in engineering design tasks. The successful design and practical applications of neural network models depend on their qualitative properties. Elaborating efficient stability is known to be of a high importance. Also, different stability notions are applied for differently behaving models. In addition, uncertainties are ubiquitous in neural network systems, and may result in performance degradation, hazards or system damage. Driven by practical needs and theoretical challenges, the rigorous handling of uncertainties in the neural network design stage is an essential research topic. In this research, the concept of robust practical stability is introduced for generalized discrete neural network models under uncertainties applied in engineering design. A robust practical stability analysis is offered using the Lyapunov function method. Since practical stability concept is more appropriate for engineering applications, the obtained results can be of a practical significance to numerous engineering design problems of diverse interest.</p>
</abstract>

In recent years, we are witnessing artificial intelligence being deployed on embedded platforms in our everyday life, including engineering design practice problems starting from early stage design ideas to the final decision. One of the most challenging problems is related to the design and implementation of neural networks in engineering design tasks. The successful design and practical applications of neural network models depend on their qualitative properties. Elaborating efficient stability is known to be of a high importance. Also, different stability notions are applied for differently behaving models. In addition, uncertainties are ubiquitous in neural network systems, and may result in performance degradation, hazards or system damage. Driven by practical needs and theoretical challenges, the rigorous handling of uncertainties in the neural network design stage is an essential research topic. In this research, the concept of robust practical stability is introduced for generalized discrete neural network models under uncertainties applied in engineering design. A robust practical stability analysis is offered using the Lyapunov function method. Since practical stability concept is more appropriate for engineering applications, the obtained results can be of a practical significance to numerous engineering design problems of diverse interest.

Neural Networks in Engineering Design: Robust Practical Stability Analysis

Department of Machine Elements and Non-metallic Constructions

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Neural Networks in Engineering Design: Robust Practical Stability Analysis"}

In recent years, we are witnessing artificial intelligence being deployed on embedded platforms in our everyday life, including engineering design practice...

Neural Networks in Engineering Design: Robust Practical Stability Analysis

Online veröffentlicht: 09. Dez. 2021

Seitenbereich: 3 - 14

Eingereicht: 10. Juni 2021

Akzeptiert: 04. Okt. 2021

DOI: https://doi.org/10.2478/cait-2021-0039

Schlüsselwörter
Neural networks, engineering design, practical stability, uncertainties, robustness

© 2021 Trayan Stamov, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Neural Networks in Engineering Design: Robust Practical Stability Analysis

Online veröffentlicht: 09. Dez. 2021

Seitenbereich: 3 - 14

Eingereicht: 10. Juni 2021

Akzeptiert: 04. Okt. 2021

DOI: https://doi.org/10.2478/cait-2021-0039

SchlüsselwörterNeural networks, engineering design, practical stability, uncertainties, robustness

© 2021 Trayan Stamov, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Neural networks, engineering design, practical stability, uncertainties, robustness