Advancing nonlinear dynamics identification with recurrent quantum neural networks: Emphasizing Lyapunov stability and adaptive learning in system analysis
Identification of nonlinear dynamic systems is a critical task in various fields. Artificial neural networks have been widely used for this purpose due to their ability to approximate complex functions. However, their computational efficiency and stability often pose challenges, especially in real-t...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824010901 |
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| author | Omar Shaheen Osama Elshazly Abdullah Baihan Walid El-Shafai Hossam Khalil |
| author_facet | Omar Shaheen Osama Elshazly Abdullah Baihan Walid El-Shafai Hossam Khalil |
| author_sort | Omar Shaheen |
| collection | DOAJ |
| description | Identification of nonlinear dynamic systems is a critical task in various fields. Artificial neural networks have been widely used for this purpose due to their ability to approximate complex functions. However, their computational efficiency and stability often pose challenges, especially in real-time applications. Quantum computation has shown potential for enhancing computational performance, but its integration with neural networks is still under investigation. The primary motivation addressed in this paper is the development of an effective strategy for synthesizing and applying recurrent quantum neural networks based on Lyapunov stability criteria (RQNN-LS) for nonlinear system identification. This model enhances the computational efficiency of recurrent neural networks by incorporating quantum computation into the neural network characteristics by using qubit neurons for data processing. Additionally, adaptive learning rates are derived based on Lyapunov stability theory for online tuning of the parameters to guarantee the stability of the proposed technique. The applicability and superiority of the presented RQNN-LS identifier are verified through the simulation and practical results of nonlinear system identification, comparing its performance with other existing identification techniques. The comparative results demonstrated significant improvements in computational efficiency with the proposed technique and highlighted the merits and superiority of the developed model over other methodologies. |
| format | Article |
| id | doaj-art-97a0062f6ab14f18b93d5af49711ae6b |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-97a0062f6ab14f18b93d5af49711ae6b2024-12-21T04:27:55ZengElsevierAlexandria Engineering Journal1110-01682024-12-01109807819Advancing nonlinear dynamics identification with recurrent quantum neural networks: Emphasizing Lyapunov stability and adaptive learning in system analysisOmar Shaheen0Osama Elshazly1Abdullah Baihan2Walid El-Shafai3Hossam Khalil4Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt; Electrical Engineering Department, Faculty of Engineering, October 6 University, 6th of October, Giza 12585, Egypt; Corresponding author at: Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt.Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt; Mechatronics Engineering Department, High Institute of Engineering, and Technology (HIET), ElMahala Elkobra, EgyptComputer Science Department, Community College, King Saud University, Riyadh 11437, Saudi ArabiaSecurity Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi Arabia; Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, EgyptDepartment of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt; Mechatronics Engineering Department, Faculty of Engineering, October 6 University, 6th of October, Giza 12585, EgyptIdentification of nonlinear dynamic systems is a critical task in various fields. Artificial neural networks have been widely used for this purpose due to their ability to approximate complex functions. However, their computational efficiency and stability often pose challenges, especially in real-time applications. Quantum computation has shown potential for enhancing computational performance, but its integration with neural networks is still under investigation. The primary motivation addressed in this paper is the development of an effective strategy for synthesizing and applying recurrent quantum neural networks based on Lyapunov stability criteria (RQNN-LS) for nonlinear system identification. This model enhances the computational efficiency of recurrent neural networks by incorporating quantum computation into the neural network characteristics by using qubit neurons for data processing. Additionally, adaptive learning rates are derived based on Lyapunov stability theory for online tuning of the parameters to guarantee the stability of the proposed technique. The applicability and superiority of the presented RQNN-LS identifier are verified through the simulation and practical results of nonlinear system identification, comparing its performance with other existing identification techniques. The comparative results demonstrated significant improvements in computational efficiency with the proposed technique and highlighted the merits and superiority of the developed model over other methodologies.http://www.sciencedirect.com/science/article/pii/S1110016824010901Quantum neural networksRecurrent quantum neural networksIdentification of nonlinear systemsQuantum computationLyapunov stability theory |
| spellingShingle | Omar Shaheen Osama Elshazly Abdullah Baihan Walid El-Shafai Hossam Khalil Advancing nonlinear dynamics identification with recurrent quantum neural networks: Emphasizing Lyapunov stability and adaptive learning in system analysis Alexandria Engineering Journal Quantum neural networks Recurrent quantum neural networks Identification of nonlinear systems Quantum computation Lyapunov stability theory |
| title | Advancing nonlinear dynamics identification with recurrent quantum neural networks: Emphasizing Lyapunov stability and adaptive learning in system analysis |
| title_full | Advancing nonlinear dynamics identification with recurrent quantum neural networks: Emphasizing Lyapunov stability and adaptive learning in system analysis |
| title_fullStr | Advancing nonlinear dynamics identification with recurrent quantum neural networks: Emphasizing Lyapunov stability and adaptive learning in system analysis |
| title_full_unstemmed | Advancing nonlinear dynamics identification with recurrent quantum neural networks: Emphasizing Lyapunov stability and adaptive learning in system analysis |
| title_short | Advancing nonlinear dynamics identification with recurrent quantum neural networks: Emphasizing Lyapunov stability and adaptive learning in system analysis |
| title_sort | advancing nonlinear dynamics identification with recurrent quantum neural networks emphasizing lyapunov stability and adaptive learning in system analysis |
| topic | Quantum neural networks Recurrent quantum neural networks Identification of nonlinear systems Quantum computation Lyapunov stability theory |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824010901 |
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