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A synergistic intelligence framework for decoupled hierarchical identification of RBF-network-based NARMAX systems
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DOI：10.1016/j.apm.2026.117237.png)
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Abstract
En 中文
• A decoupled hierarchical framework for RBF-NARMAX system identification. • Adaptive step-size matrix gradient algorithm for rapid parameter estimation. • PSO-assisted gradient search optimizes RBF hyperparameters effectively. • EEG data validate long-horizon cortical response prediction accuracy.
Keywords:
Nonlinear system identification
RBF-network-based NARMAX system
Gradient iterative
Particle swarm optimization assisted optimization
Electroencephalography cortical response modeling
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