Full Text:   <537>

Suppl. Mater.: 

CLC number: O231

On-line Access: 2024-12-26

Received: 2023-12-03

Revision Accepted: 2024-03-21

Crosschecked: 2025-01-24

Cited: 0

Clicked: 1250

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Lakshminarayana JANJANAM

https://orcid.org/0000-0001-5340-4058

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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.11 P.1515-1535

http://doi.org/10.1631/FITEE.2300817


Enhancing modelling accuracy of cascaded spline adaptive filters using the remora optimisation algorithm: application to real-time systems


Author(s):  Lakshminarayana JANJANAM, Suman Kumar SAHA, Rajib KAR

Affiliation(s):  JNTUK Recognized Research Center, Department of Electronics & Communication Engineering, Sasi Institute of Technology & Engineering, Andhra Pradesh534101,India; more

Corresponding email(s):   jlphd.nitrr@gmail.com, namus.ahas@gmail.com, rajibkarece@gmail.com

Key Words:  Cascaded spline adaptive filter, Nonlinear system identification, Remora optimisation algorithm



Abstract: 
We first introduce a new approach for optimising a cascaded spline adaptive filter (CSAF) to identify unknown nonlinear systems by using a meta-heuristic optimisation algorithm (MOA). The CSAF architecture combines Hammerstein and Wiener systems, where the nonlinear blocks are implemented with the spline network. The algorithms used optimise the weights of the spline interpolation function and linear filter by using an adequately weighted cost function, leading to improved filter stability, steady state performance, and guaranteed convergence to globally optimal solutions. We investigate two CSAF architectures: Hammerstein‍–‍Wiener SAF (HW-SAF) and Wiener‍–‍Hammerstein SAF (WH-SAF) structures. These architectures have been designed using gradient-based approaches which are inefficient due to poor convergence speed, and produce suboptimal solutions in a Gaussian noise environment. To avert these difficulties, we estimate the design parameters of the CSAF architecture using four independent MOAs: differential evolution (DE), brainstorm optimisation (BSO), multi-verse optimiser (MVO), and a recently proposed remora optimisation algorithm (ROA). In ROA, the remora factor’s control parameters produce near-global optimal parameters with a higher convergence speed. ROA also ensures the most balanced exploration and exploitation phases compared to DE-, BSO-, and MVO-based design approaches. Finally, the identification results of three numerical and industry-specific benchmark systems, including coupled electric drives, a thermic wall, and a continuous stirred tank reactor, are presented to emphasise the effectiveness of the ROA-based CSAF design.

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