Оригинал (Original)
Автори: Стамов, Т. Г.
Заглавие: Neural Networks in Engineering Design: Robust Practical Stability Analysis
Ключови думи: Neural networks, engineering design, practical stability, un

Абстракт: 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 gener

Библиография

    Издание

    Cybernetics and Information Technologies, том 21, брой 4, стр. стр. 3-14, 2021, България, BULGARIAN ACADEMY OF SCIENCES
    		Autors: Stamov, T. G.
    Title: Neural Networks in Engineering Design: Robust Practical Stability Analysis
    Keywords: Neural networks, engineering design, practical stability, uncertainties, robustness.

    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 gener

    References

      Issue

      Cybernetics and Information Technologies, vol. 21, issue 4, pp. 3-14, 2021, Bulgaria, BULGARIAN ACADEMY OF SCIENCES

      Цитирания (Citation/s):
      1. Discrete Bidirectional Associative Memory Neural Networks of the Cohen–Grossberg Type for Engineering Design Symmetry Related Problems: Practical Stability of Sets Analysis - 2022 - в издания, индексирани в Scopus или Web of Science
      2. Extended Stability and Control Strategies for Impulsive and Fractional Neural Networks: A Review of the Recent Results - 2023 - в издания, индексирани в Scopus или Web of Science
      3. A practical observer for state and sensor fault reconstruction of a class of fractional‐order nonlinear systems - 2023 - в издания, индексирани в Scopus или Web of Science
      4. Impulsive Controllers Design for the Practical Stability Analysis of Gene Regulatory Networks with Distributed Delays - 2023 - в издания, индексирани в Scopus или Web of Science
      5. Formulation of Impulsive Ecological Systems Using the Conformable Calculus Approach: Qualitative Analysis - 2023 - в издания, индексирани в Scopus или Web of Science

      Вид: публикация в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus