A novel machine learning algorithm for interval systems approximation based on artificial neural network
In recent years, order-reduction techniques based on artificial intelligence algorithms have become a topic of interest in the structural dynamics community. In this paper, we describe a new artificial intelligence technique based on the artificial neural network used to reduce a large interval syst...
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| Veröffentlicht in: | Journal of intelligent manufacturing 2023-06, Vol.34 (5), p.2171-2184 |
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| container_title | Journal of intelligent manufacturing |
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| creator | Zerrougui, Raouf Adamou-Mitiche, Amel B. H. Mitiche, Lahcene |
| description | In recent years, order-reduction techniques based on artificial intelligence algorithms have become a topic of interest in the structural dynamics community. In this paper, we describe a new artificial intelligence technique based on the artificial neural network used to reduce a large interval system. Applied to reduce the degree of the polynomial numerator and denominator each separately, by allowing them to learn automatically from the original system, this machine learning phase allows the algorithm to improve over time and control performance of the approximation, maintaining as much as possible the stability of the dynamic system, and reducing errors between the original system and the reduced system that are presented as a very acceptable approximation, a comparison study is presented between existing works and the proposed technique, with the help of examples from literature. |
| doi_str_mv | 10.1007/s10845-021-01874-0 |
| format | Article |
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| subjects | Advanced manufacturing technologies Algorithms Approximation Artificial intelligence Artificial neural networks Business and Management Control Dynamic stability Dynamical systems Machine learning Machines Manufacturing Mathematical analysis Mechatronics Methods Neural networks Polynomials Processes Production Robotics Simulation Spectrum analysis |
| title | A novel machine learning algorithm for interval systems approximation based on artificial neural network |
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