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Partially-coupled nonlinear parameter optimization algorithm for a class of multivariate hybrid models

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  • Zhou, Yihong
  • Zhang, Xiao
  • Ding, Feng

Abstract

A key to the analysis and design of a dynamic system is to establish a suitable mathematical model of the system. This paper investigates the parameter optimization problem of a class of radial basis function-based multivariate hybrid models. Taking into account the high dimensions of the models and different forms of the parameters, the original identification model is separated into several regressive sub-identification models according to the characteristics of model outputs. Some auxiliary models are constructed to solve the unmeasurable noise terms in the information matrices. For the purpose of eliminating the redundant computation and to deal with the associate terms caused by the model decomposition, inspired by the coupling concept, a partially-coupled nonlinear parameter optimization algorithm is proposed for the multivariate hybrid models. Through the computational efficiency analysis and numerical simulation verification, it is shown that the proposed algorithm has low computational complexity and high parameter estimation accuracy.

Suggested Citation

  • Zhou, Yihong & Zhang, Xiao & Ding, Feng, 2022. "Partially-coupled nonlinear parameter optimization algorithm for a class of multivariate hybrid models," Applied Mathematics and Computation, Elsevier, vol. 414(C).
    • Handle: RePEc:eee:apmaco:v:414:y:2022:i:c:s0096300321007475
    DOI: 10.1016/j.amc.2021.126663
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    References listed on IDEAS

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    1. Ling Xu & Feng Ding & Quanmin Zhu, 2019. "Hierarchical Newton and least squares iterative estimation algorithm for dynamic systems by transfer functions based on the impulse responses," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(1), pages 141-151, January.
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    3. Xue-Bo Jin & Wei-Zhen Zheng & Jian-Lei Kong & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Seng Lin, 2021. "Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization," Energies, MDPI, vol. 14(6), pages 1-18, March.
    4. Volna, Eva & Jarusek, Robert & Kotyrba, Martin & Zacek, Jaroslav, 2021. "Training set fuzzification based on histogram to increase the performance of a neural network," Applied Mathematics and Computation, Elsevier, vol. 398(C).
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    Cited by:

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    2. Jing, Shaoxue, 2023. "Time-delay Hammerstein system identification using modified cross-correlation method and variable stacking length multi-error algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 288-300.
    3. Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Kiani, Adiqa Kausar & Raja, Muhammad Asif Zahoor & Chaudhary, Iqra Ishtiaq & Pinto, Carla M.A., 2022. "Design of auxiliary model based normalized fractional gradient algorithm for nonlinear output-error systems," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    4. Tang, Jia, 2023. "Fractional gradient descent algorithm for switching models using self-organizing maps: One set data or all the collected data," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).

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