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Kernel-based system identification using generalized orthogonal basis functions and meta-heuristic techniques.

Wenfeng Li1, Yang Liu2

  • 1Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150006, China.

ISA Transactions
|August 26, 2025
PubMed
Summary
This summary is machine-generated.

This study presents new methods for kernel-based regularization (KRM) in system identification. Enhanced kernel design and meta-heuristic hyperparameter optimization improve model estimation performance.

Keywords:
Generalized orthogonal basis functionsKernel designKernel-based regularizationMeta-heuristic techniquesSystem identification

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Area of Science:

  • Control Systems Engineering
  • Machine Learning
  • Signal Processing

Background:

  • Kernel-based regularization methods (KRM) are pivotal in system identification for both causal and non-causal systems.
  • Key challenges in KRM include kernel design and hyperparameter estimation.
  • Existing KRM approaches require robust solutions for these challenges.

Discussion:

  • This paper introduces a novel framework for causal kernel design using generalized orthogonal basis functions (GOBFs), extended to non-causal systems.
  • Meta-heuristic algorithms, exemplified by the grey wolf optimization (GWO), are explored for hyperparameter estimation in KRM.
  • The GWO algorithm is enhanced with nonlinear weight updates and genetic algorithm-inspired (GA) crossover/mutation for improved search accuracy.

Key Insights:

  • The proposed GOBF framework offers a structured approach to kernel design in KRM.
  • Enhanced GWO demonstrates superior performance in hyperparameter estimation for KRM.
  • The integrated KRM approach shows significant improvements in system identification model performance.

Outlook:

  • Future research can explore other meta-heuristic algorithms for KRM hyperparameter optimization.
  • The GOBF framework may be adaptable to other kernel-based learning methods.
  • Further validation of the enhanced KRM across diverse system identification problems is warranted.