Feedback-based optimization of feed-forward neural network for the modeling of complex nonlinear dynamical systems using novel APSOBP algorithm
This work proposes a novel hybrid Adaptive Particle Swarm Optimization-Back-propagation algorithm for training feed-forward neural networks to identify nonlinear dynamical systems. The approach begins by using Particle Swarm Optimization to optimize the network weights, followed by back propagation...
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| Vydáno v: | ISA transactions Ročník 167; číslo Pt B; s. 1637 |
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Elsevier Ltd
01.12.2025
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| ISSN: | 0019-0578, 1879-2022, 1879-2022 |
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| Abstract | This work proposes a novel hybrid Adaptive Particle Swarm Optimization-Back-propagation algorithm for training feed-forward neural networks to identify nonlinear dynamical systems. The approach begins by using Particle Swarm Optimization to optimize the network weights, followed by back propagation to fine-tune the optimized weights, thereby improving the overall solution quality. To prevent early convergence, Particle Swarm Optimization parameters such as inertia weight and other hyperparameters are dynamically adjusted based on a performance index, which is calculated as the difference between the fitness value of the global best solution across consecutive iterations. Convergence analysis using Lyapunov stability theory is also conducted to ensure the proposed algorithm converges to a stable solution. The proposed hybrid approach is evaluated on three benchmark nonlinear problems to validate its effectiveness. Experimental results demonstrate that the hybrid algorithm outperforms traditional Particle Swarm Optimization and back-propagation algorithms in terms of convergence, accuracy, and robustness.
•A novel hybrid adaptive particle swarm optimization-back propagation algorithm (APSOBP) for training feed-forward neural networks to identify nonlinear dynamical systems is proposed.•Hybrid adaptive particle swarm optimization and back-propagation algorithm are applied to optimize the weights of the feed-forward neural network.•To avoid premature convergence and improve performance, the inertia weight and cognitive and social components of PSO are dynamically adjusted based on a performance index criterion.•The experimental results of training feed-forward neural network using the APSOBP algorithm are compared to traditional feed-forward neural network training using PSO (FFNN-PSO) and BP (FFNN-BP) algorithms.•The effect of disturbance tolerance on feed-forward neural networks using the APSOBP algorithm is also discussed in the study. |
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| AbstractList | This work proposes a novel hybrid Adaptive Particle Swarm Optimization-Back-propagation algorithm for training feed-forward neural networks to identify nonlinear dynamical systems. The approach begins by using Particle Swarm Optimization to optimize the network weights, followed by back propagation to fine-tune the optimized weights, thereby improving the overall solution quality. To prevent early convergence, Particle Swarm Optimization parameters such as inertia weight and other hyperparameters are dynamically adjusted based on a performance index, which is calculated as the difference between the fitness value of the global best solution across consecutive iterations. Convergence analysis using Lyapunov stability theory is also conducted to ensure the proposed algorithm converges to a stable solution. The proposed hybrid approach is evaluated on three benchmark nonlinear problems to validate its effectiveness. Experimental results demonstrate that the hybrid algorithm outperforms traditional Particle Swarm Optimization and back-propagation algorithms in terms of convergence, accuracy, and robustness.This work proposes a novel hybrid Adaptive Particle Swarm Optimization-Back-propagation algorithm for training feed-forward neural networks to identify nonlinear dynamical systems. The approach begins by using Particle Swarm Optimization to optimize the network weights, followed by back propagation to fine-tune the optimized weights, thereby improving the overall solution quality. To prevent early convergence, Particle Swarm Optimization parameters such as inertia weight and other hyperparameters are dynamically adjusted based on a performance index, which is calculated as the difference between the fitness value of the global best solution across consecutive iterations. Convergence analysis using Lyapunov stability theory is also conducted to ensure the proposed algorithm converges to a stable solution. The proposed hybrid approach is evaluated on three benchmark nonlinear problems to validate its effectiveness. Experimental results demonstrate that the hybrid algorithm outperforms traditional Particle Swarm Optimization and back-propagation algorithms in terms of convergence, accuracy, and robustness. This work proposes a novel hybrid Adaptive Particle Swarm Optimization-Back-propagation algorithm for training feed-forward neural networks to identify nonlinear dynamical systems. The approach begins by using Particle Swarm Optimization to optimize the network weights, followed by back propagation to fine-tune the optimized weights, thereby improving the overall solution quality. To prevent early convergence, Particle Swarm Optimization parameters such as inertia weight and other hyperparameters are dynamically adjusted based on a performance index, which is calculated as the difference between the fitness value of the global best solution across consecutive iterations. Convergence analysis using Lyapunov stability theory is also conducted to ensure the proposed algorithm converges to a stable solution. The proposed hybrid approach is evaluated on three benchmark nonlinear problems to validate its effectiveness. Experimental results demonstrate that the hybrid algorithm outperforms traditional Particle Swarm Optimization and back-propagation algorithms in terms of convergence, accuracy, and robustness. This work proposes a novel hybrid Adaptive Particle Swarm Optimization-Back-propagation algorithm for training feed-forward neural networks to identify nonlinear dynamical systems. The approach begins by using Particle Swarm Optimization to optimize the network weights, followed by back propagation to fine-tune the optimized weights, thereby improving the overall solution quality. To prevent early convergence, Particle Swarm Optimization parameters such as inertia weight and other hyperparameters are dynamically adjusted based on a performance index, which is calculated as the difference between the fitness value of the global best solution across consecutive iterations. Convergence analysis using Lyapunov stability theory is also conducted to ensure the proposed algorithm converges to a stable solution. The proposed hybrid approach is evaluated on three benchmark nonlinear problems to validate its effectiveness. Experimental results demonstrate that the hybrid algorithm outperforms traditional Particle Swarm Optimization and back-propagation algorithms in terms of convergence, accuracy, and robustness. •A novel hybrid adaptive particle swarm optimization-back propagation algorithm (APSOBP) for training feed-forward neural networks to identify nonlinear dynamical systems is proposed.•Hybrid adaptive particle swarm optimization and back-propagation algorithm are applied to optimize the weights of the feed-forward neural network.•To avoid premature convergence and improve performance, the inertia weight and cognitive and social components of PSO are dynamically adjusted based on a performance index criterion.•The experimental results of training feed-forward neural network using the APSOBP algorithm are compared to traditional feed-forward neural network training using PSO (FFNN-PSO) and BP (FFNN-BP) algorithms.•The effect of disturbance tolerance on feed-forward neural networks using the APSOBP algorithm is also discussed in the study. |
| Author | Kumar, Rajesh Jaint, Bhavnesh R., Shobana |
| Author_xml | – sequence: 1 givenname: Shobana surname: R. fullname: R., Shobana email: r.shobana@galgotiacollege.edu organization: Department of Electrical Engineering, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi 110042, India – sequence: 2 givenname: Rajesh orcidid: 0000-0001-7172-1081 surname: Kumar fullname: Kumar, Rajesh email: rajeshmahindru23@nitkkr.ac.in organization: Department of Electrical Engineering, National Institute of Technology, Kurukshetra 136119, India – sequence: 3 givenname: Bhavnesh surname: Jaint fullname: Jaint, Bhavnesh email: bhavneshjaint@dtu.ac.in organization: Department of Electrical Engineering, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi 110042, India |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/41058408$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/s00500-024-09814-9 10.1016/j.jer.2023.09.016 10.1109/41.538609 10.3390/w16081184 10.1016/j.cmpb.2025.108776 10.3390/math10091611 10.1016/j.neucom.2003.12.006 10.3390/electronics12030491 10.1016/j.isatra.2019.01.012 10.1007/s00521-011-0524-7 10.1016/j.mejo.2023.105834 10.1016/j.apm.2010.08.008 10.1080/002071797224838 10.1109/72.80202 10.1007/s00500-023-09187-5 10.1109/TNNLS.2016.2616413 10.1038/323533a0 10.1155/2021/6648432 10.1016/j.asoc.2022.109756 10.1109/ACCESS.2023.3272223 10.1016/j.measurement.2024.114451 10.1007/s12205-021-2223-y |
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| Keywords | Feed forward neural network Adaptive particle swarm optimization-back-propagation algorithm Back-propagation algorithm Particle swarm optimization algorithm |
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