ArticleBiomimetics (Basel, Switzerland)2023
Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model.
Article in Biomimetics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Drosophila Optimization Algorithm Based on Chaotic Development Mechanism and Orthogonal Learning Strategy for Reservoir Optimization.Biomimetics (Basel, Switzerland) · 2026Article
- Hate speech detection using pretrained DeBERTaV3 optimized with Grey Wolf Optimizer: a hybrid approach for social media content moderation.Scientific reports · 2026Article
- Article
- Exploring the potential of 5G uplink communication: Synergistic integration of joint power control, user grouping, and multi-learning Grey Wolf Optimizer.Scientific reports · 2024Article
- A novel hybrid adaptive differential evolution for global optimization.Scientific reports · 2024Article
- Multi-kernel support vector regression with improved moth-flame optimization algorithm for software effort estimation.Scientific reports · 2024Article
- Face Image Segmentation Using Boosted Grey Wolf Optimizer.Biomimetics (Basel, Switzerland) · 2023Article
- Resistance-capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial engineering computation problems.Scientific reports · 2023Article
- Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson's Disease Model.Biomimetics (Basel, Switzerland) · 2023Article
- Uncertainty analysis and optimization of laser thermal pain treatment.Scientific reports · 2023Article
- Mother optimization algorithm: a new human-based metaheuristic approach for solving engineering optimization.Scientific reports · 2023Article
- A novel hermit crab optimization algorithm.Scientific reports · 2023Article
- A novel combined intelligent algorithm prediction model for the tunnel surface settlement.Scientific reports · 2023Article
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5 authors.
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Abstract
In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels. The comparative performance analyses with standard counterparts indicate the worth of the ICGWO for ARX model identification, while the statistical analyses endorse the efficacy of the proposed chaotic scheme in terms of accuracy, robustness, and reliability.
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