Optimization Techniques for System Identification and Filter Design

The process of system identification involves constructing mathematical models of dynamic systems based on observed input–output data, while filter design focuses on synthesising digital or analogue filters to manipulate signals according to prescribed frequency characteristics. Both problems can be cast as optimisation tasks in which a cost function—often representing estimation error or deviation from target frequency response—is minimised. Traditional approaches have included gradient-based and convex methods, but nonconvexity and multimodality of the associated error surfaces frequently demand global search strategies. Over the past decade, a rich ecosystem of metaheuristic algorithms has emerged—ranging from evolutionary computation and swarm intelligence to physics-inspired techniques—each offering distinct advantages in terms of convergence behaviour, robustness to local minima and computational cost. Recent advances have emphasised hybridisation of complementary heuristics, stability constraints for recursive filter structures, adaptive parameter control and the use of chaos or opposition-based schemes to enhance population diversity. The global significance of these developments spans aerospace control, telecommunications, biomedical imaging and industrial process monitoring, where reliable model estimation and filter performance are critical under stringent real-time and stability requirements.

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Recent studies have demonstrated the efficacy of manta ray foraging optimisation for reduced-order infinite impulse response (IIR) system identification. Key algorithmic modifications include a stability-ensuring pole-finding routine and adaptive control of foraging parameters, yielding improved convergence rates and robust performance across benchmark models even under wide design bounds.

Another line of work introduces a chaotic opposition-based whale optimisation method for adaptive IIR model estimation. By combining a logistic map-driven oppositional initia­lisation with the standard humpback-whale hunting metaphor, this approach achieves faster convergence and lower mean-square error compared to conventional nature-inspired techniques in various simulation scenarios.

Progress has also been made using a multistrategy enhanced slime mould algorithm for digital IIR filter design and system identification. This hybrid employs chaotic initialisation to boost early diversity, orthogonal learning for informed local search and boundary-reset to maintain exploratory capacity. Benchmark results indicate superior balance between convergence speed, accuracy and robustness relative to state-of-the-art metaheuristics.

Optimization Techniques for System Identification and Filter Design publication trend

The graph below shows the total number of articles in optimization techniques for system identification and filter design across all publications each year (not limited to Nature Index journals).

Technical terms

System identification: The procedure of developing a mathematical representation of a dynamic system from observed data.

Filter design: The synthesis of a system (digital or analogue) that modifies signal spectra according to specified passband and stopband criteria.

Infinite impulse response (IIR): A filter structure whose output depends on both current and past inputs and past outputs, leading to recursive behaviour.

Metaheuristic algorithm: A non-deterministic optimisation method inspired by natural or physical processes, suited to multimodal and high-dimensional search spaces.

Error surface: A hypersurface representing the value of an objective function (such as estimation or design error) across the parameter space.

Convergence rate: The speed at which an optimisation algorithm approaches its best solution or equilibrium state.

References

1. Reduced order infinite impulse response system identification using manta ray foraging optimization. Alexandria Engineering Journal (2024).
2. Adaptive IIR model identification using chaotic opposition-based whale optimization algorithm. Journal of Electrical Systems and Information Technology (2023).
3. An Enhanced Slime Mould Algorithm and Its Application for Digital IIR Filter Design. Discrete Dynamics in Nature and Society (2021).