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: 1044

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Lakshminarayana JANJANAM

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

-   Go to

Article info.
Open peer comments

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


Lakshminarayana JANJANAM, Suman Kumar SAHA, Rajib KAR. Enhancing modelling accuracy of cascaded spline adaptive filters using the remora optimisation algorithm: application to real-time systems[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(11): 1515-1535.

@article{title="Enhancing modelling accuracy of cascaded spline adaptive filters using the remora optimisation algorithm: application to real-time systems",
author="Lakshminarayana JANJANAM, Suman Kumar SAHA, Rajib KAR",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="25",
number="11",
pages="1515-1535",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300817"
}

%0 Journal Article
%T Enhancing modelling accuracy of cascaded spline adaptive filters using the remora optimisation algorithm: application to real-time systems
%A Lakshminarayana JANJANAM
%A Suman Kumar SAHA
%A Rajib KAR
%J Frontiers of Information Technology & Electronic Engineering
%V 25
%N 11
%P 1515-1535
%@ 2095-9184
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300817

TY - JOUR
T1 - Enhancing modelling accuracy of cascaded spline adaptive filters using the remora optimisation algorithm: application to real-time systems
A1 - Lakshminarayana JANJANAM
A1 - Suman Kumar SAHA
A1 - Rajib KAR
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 11
SP - 1515
EP - 1535
%@ 2095-9184
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2300817


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.

基于印鱼优化算法提升级联样条自适应滤波器的建模精度及其在实时系统中的应用

Lakshminarayana JANJANAM 1 ,Suman Kumar SAHA 2 ,Rajib KAR 3
1 萨西技术与工程学院电子与通信工程系JNTUK认证研究中心,印度安得拉邦,534101
2 拉普尔国立理工学院电子与通信工程系,印度查蒂斯加尔邦,492010
3 杜尔加普尔国立理工学院电子与通信工程系,印度西孟加拉邦,713209
摘要: 介绍了一种新的优化级联样条自适应滤波器(CSAF)方法,通过使用元启发式优化算法(MOA)识别未知的非线性系统。CSAF架构结合了汉默斯坦和维纳系统,其中非线性块通过样条网络实现。所用算法通过适当加权的成本函数优化样条插值函数和线性滤波器的权重,从而提高滤波器的稳定性、稳态性以及全局最优解的收敛性。本文研究了两种CSAF架构:汉默斯坦-维纳样条自适应滤波器(HW-SAF)和维纳-汉默斯坦样条自适应滤波器(WH-SAF)结构。这两种架构是基于梯度方法设计的,其收敛速度慢,效率低,且在高斯噪声环境下会产生次优解。为克服以上困难,本文采用4种独立的MOA以估计CSAF架构的设计参数:差分进化(DE)、头脑风暴优化(BSO)、多元宇宙优化器(MVO)以及最近提出的印鱼优化算法(ROA)。在ROA中,印鱼因子的控制参数能以更高的收敛速度产生接近全局最优的参数。与基于DE、BSO和MVO的方法相比,ROA确保了探索和开发阶段的平衡。最后,3个数值和特定行业基准系统(即耦合电驱动、热壁和连续搅拌槽反应器)的识别结果表明了基于印鱼优化算法CSAF的有效性。

关键词:级联样条自适应滤波器;非线性系统辨识;印鱼优化算法

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Braik M, Hammouri A, Atwan J, et al., 2022. White shark optimizer: a novel bio-inspired meta-heuristic algorithm for global optimization problems. Knowl-Based Syst, 243:108457.

[2]Chaudhary NI, Manzar MA, Raja MAZ, 2019. Fractional Volterra LMS algorithm with application to Hammerstein control autoregressive model identification. Neur Comput Appl, 31(9):5227-5240.

[3]Chen ZY, Meng YH, Chen T, 2022. NN model-based evolved control by DGM model for practical nonlinear systems. Expert Syst Appl, 193:115873.

[4]De Moor B, 2004. Database for Identification of Systems. https://homes.‍esat.kuleuven.‍be/~smc/~daisy/ [Accessed on Nov. 15, 2023].

[5]Esmaeilani L, Ghaisari J, Bagherzadeh MA, 2021. Hammerstein‍–‍Wiener identification of industrial plants: a pressure control valve case study. IET Contr Theory Appl, 15(3):416-431.

[6]Gao Y, Zhao HQ, Zhu YY, et al., 2023. Spline adaptive filtering algorithm-based generalized maximum correntropy and its application to nonlinear active noise control. Circ Syst Signal Process, 42(11):6636-6659.

[7]Gao Y, Zhao HQ, Zhu YY, et al., 2024. The q-gradient LMS spline adaptive filtering algorithm and its variable step-size variant. Inform Sci, 658:119983.

[8]Garcia-Vega S, Zeng XJ, Keane J, 2020. Stock returns prediction using kernel adaptive filtering within a stock market interdependence approach. Expert Syst Appl, 160:113668.

[9]Guan SH, Biswal B, 2023. Spline adaptive filtering algorithm based on different iterative gradients: performance analysis and comparison. J Autom Intell, 2(1):1-13.

[10]Guan SH, Cheng Q, Zhao Y, et al., 2022. Spline adaptive filtering algorithm based on Heaviside step function. Signal Image Video Process, 16(5):1333-1343.

[11]Guo WY, Zhi YF, 2022. Nonlinear spline adaptive filtering against non-Gaussian noise. Circ Syst Signal Process, 41(1):579-596.

[12]Guo WY, Zhi YF, Feng K, 2022. Nonlinear spline prioritization optimization adaptive filter with arctangent-exponential hyperbolic cosine. Nonl Dynam, 110(1):611-621.

[13]Hafezi Z, Arefi MM, 2019. Recursive generalized extended least squares and RML algorithms for identification of bilinear systems with ARMA noise. ISA Trans, 88:50-61.

[14]Hammar K, Djamah T, Bettayeb M, 2019. Nonlinear system identification using fractional Hammerstein‍–‍Wiener models. Nonl Dynam, 98(3):2327-2338.

[15]Hegde V, Radhakrishnan C, Krusienski DJ, et al., 2002a. Architectures and algorithms for nonlinear adaptive filters. 36th Asilomar Conf on Signals, Systems and Computers, p.1015-1018.

[16]Hegde V, Radhakrishnan C, Krusienski D, et al., 2002b. Series-cascade nonlinear adaptive filters. 45th Midwest Symp on Circuits and Systems, p.III-219-III-222.

[17]Holm S, 1979. A simple sequentially rejective multiple test procedure. Scand J Stat, 6(2):65-70.

[18]Janjanam L, Saha SK, Kar R, et al., 2021a. An efficient identification approach for highly complex non-linear systems using the evolutionary computing method based Kalman filter. AEU Int J Electron Commun, 138:153890.

[19]Janjanam L, Saha SK, Kar R, et al., 2021b. Global gravitational search algorithm-aided Kalman filter design for Volterra-based nonlinear system identification. Circ Syst Signal Process, 40(5):2302-2334.

[20]Janjanam L, Saha SK, Kar R, et al., 2022a. Hammerstein-Wiener nonlinear system identification by using honey badger algorithm hybridized Sage-Husa adaptive Kalman filter with real-time applications. AEU Int J Electron Commun, 151:154218.

[21]Janjanam L, Saha SK, Kar R, et al., 2022b. Improving the modelling efficiency of Hammerstein system using Kalman filter and its parameters optimised using social mimic algorithm: application to heating and cascade water tanks. J Franklin Inst, 359(3):1239-1273.

[22]Janjanam L, Saha SK, Kar R, et al., 2022c. Optimal design of cascaded Wiener-Hammerstein system using a heuristically supervised discrete Kalman filter with application on benchmark problems. Expert Syst Appl, 200:117065.

[23]Janjanam L, Saha SK, Kar R, et al., 2022d. Wiener model-based system identification using moth flame optimised Kalman filter algorithm. Signal Image Video Process, 16(5):1425-1433.

[24]Janjanam L, Saha SK, Kar R, 2023. Optimal design of Hammerstein cubic spline filter for nonlinear system modeling based on snake optimiser. IEEE Trans Ind Electron, 70(8):8457-8467.

[25]Jeraj J, Mathews VJ, 2006. A stable adaptive Hammerstein filter employing partial orthogonalization of the input signals. IEEE Trans Signal Process, 54(4):1412-1420.

[26]Jia HM, Lang CB, Oliva D, et al., 2019. Dynamic Harris hawks optimization with mutation mechanism for satellite image segmentation. Remote Sens, 11(12):1421.

[27]Jia HM, Peng XX, Lang CB, 2021. Remora optimization algorithm. Expert Syst Appl, 185:115665.

[28]Jia HM, Rao HH, Wen CS, et al., 2023. Crayfish optimization algorithm. Artif Intell Rev, 56(2):1919-1979.

[29]Jia L, Feng QL, 2017. Combined separable signals based neuro-fuzzy Hammerstein‍–‍Wiener model. Memet Comput, 9(3):245-259.

[30]Li LW, Ren XM, 2018. Identification of nonlinear Wiener-Hammerstein systems by a novel adaptive algorithm based on cost function framework. ISA Trans, 80:146-159.

[31]Li WQ, Xu M, Tang JS, et al., 2023. Robust frequency domain spline adaptive filtering based on the half-quadratic criterion: performance analysis and applications. IEEE Trans Instrum Meas, 72:6503513.

[32]Lightbody G, Irwin GW, 1997. Nonlinear control structures based on embedded neural system models. IEEE Trans Neur Netw, 8(3):553-567.

[33]Liu C, Zhao HQ, 2023. A 2D-LUT scheme design for complex-valued spline adaptive filter. IEEE Trans Circ Syst II Expr Briefs, 70(8):3154-3158.

[34]Liu Q, Tang XM, Li JH, et al., 2021. Identification of Wiener–Hammerstein models based on variational Bayesian approach in the presence of process noise. J Franklin Inst, 358(10):5623-5638.

[35]Mehmood A, Raja MAZ, 2023. Novel design of weighted differential evolution for parameter estimation of Hammerstein-Wiener systems. J Adv Res, 43:123-136.

[36]Mehmood A, Zameer A, Chaudhary NI, et al., 2019a. Backtracking search heuristics for identification of electrical muscle stimulation models using Hammerstein structure. Appl Soft Comput, 84:105705.

[37]Mehmood A, Chaudhary NI, Zameer A, et al., 2019b. Backtracking search optimization heuristics for nonlinear Hammerstein controlled auto regressive auto regressive systems. ISA Trans, 91:99-113.

[38]Mirjalili S, Lewis A., 2016. The whale optimization algorithm. Adv Eng Softw, 95:51-67.

[39]Mirjalili S, Mirjalili SM, Hatamlou A, 2016. Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neur Comput Appl, 27(2):495-513.

[40]Mishra BP, Panigrahi T, Wilson AM, et al., 2023. Nonlinear channel estimation based on robust distributed Hammerstein spline adaptive technique in wireless sensor network. Dig Signal Process, 132:103791.

[41]Nayak C, Saha SK, Kar R, et al., 2019. An efficient and robust digital fractional order differentiator based ECG pre-processor design for QRS detection. IEEE Trans Biomed Circ Syst, 13(4):682-696.

[42]Pal PS, Kar R, Mandal D, et al., 2017. Parametric identification with performance assessment of Wiener systems using brain storm optimization algorithm. Circ Syst Signal Process, 36(8):3143-3181.

[43]Patel V, Gandhi V, Heda S, et al., 2016. Design of adaptive exponential functional link network-based nonlinear filters. IEEE Trans Circ Syst I Reg Pap, 63(9):1434-1442.

[44]Raja MAZ, Aslam MS, Chaudhary NI, et al., 2018. Bio-inspired heuristics hybrid with interior-point method for active noise control systems without identification of secondary path. Front Inform Technol Electron Eng, 19(2):‍246-259.

[45]Sankar S, Kar A, Burra S, et al., 2020. Nonlinear acoustic echo cancellation with kernelized adaptive filters. Appl Acoust, 166:107329.

[46]Scarpiniti M, Comminiello D, Parisi R, et al., 2013. Nonlinear spline adaptive filtering. Signal Process, 93(4):772-783.

[47]Scarpiniti M, Comminiello D, Parisi R, et al., 2014. Hammerstein uniform cubic spline adaptive filters: learning and convergence properties. Signal Process, 100:112-123.

[48]Scarpiniti M, Comminiello D, Parisi R, et al., 2015a. Nonlinear system identification using IIR spline adaptive filters. Signal Process, 108:30-35.

[49]Scarpiniti M, Comminiello D, Parisi R, et al., 2015b. Novel cascade spline architectures for the identification of nonlinear systems. IEEE Trans Circ Syst I Reg Pap, 62(7):1825-1835.

[50]Scarpiniti M, Comminiello D, Parisi R, et al., 2018. Spline adaptive filters: theory and applications. In: Comminiello D, Príncipe JC (Eds.), Adaptive Learning Methods for Nonlinear System Modeling. Elsevier, Amsterdam, the Netherlands, p.‍47-69.

[51]Schetzen M, 1981. Nonlinear system modeling based on the Wiener theory. Proc IEEE, 69(12):1557-1573.

[52]Shadravan S, Naji HR, Bardsiri VK, 2019. The sailfish optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng Appl Artif Intell, 80:20-34.

[53]Shi YH, 2011. Brain storm optimization algorithm. Proc 2nd Int Conf on Advances in Swarm Intelligence, p.303-309.

[54]Storn R, Price K, 1997. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim, 11(4):341-359.

[55]Wang YJ, Tang SH, Gu XB, 2022. Parameter estimation for nonlinear Volterra systems by using the multi-innovation identification theory and tensor decomposition. J Franklin Inst, 359(2):1782-1802.

[56]Wigren T, Schoukens J, 2013. Three free data sets for development and benchmarking in nonlinear system identification. European Control Conf, p.2933-2938.

[57]Xu L, Jia HM, Lang CB, et al., 2019. A novel method for multilevel color image segmentation based on dragonfly algorithm and differential evolution. IEEE Access, 7:‍19502-19538.

[58]Yadav S, Saha SK, Kar R, et al., 2022. EEG/ERP signal enhancement through an optimally tuned adaptive filter based on marine predators algorithm. Biomed Signal Process Contr, 73:103427.

[59]Yadav S, Saha SK, Kar R, 2023. An application of the Kalman filter for EEG/ERP signal enhancement with the autoregressive realisation. Biomed Signal Process Contr, 86:105213.

[60]Yan H, Zhong CQ, Wu YH, et al., 2023. A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimisation for mixed-variable CNN hyperparameter automatic search. Front Inform Technol Electron Eng, 24(11):1557-1573.

[61]Yang LD, Liu JX, Yan RQ, et al., 2019. Spline adaptive filter with arctangent-momentum strategy for nonlinear system identification. Signal Process, 164:99-109.

[62]Yu T, Li WQ, Yu Y, et al., 2021. Robust spline adaptive filtering based on accelerated gradient learning: design and performance analysis. Signal Process, 183:107965.

[63]Yu T, Tan SJ, Li WQ, et al., 2024. Performance analysis of robust subband Hammerstein spline adaptive filter. Circ Syst Signal Process, 43(1):368-387.

[64]Zhang YF, Zhao ZD, Deng YJ, et al., 2021. ECGID: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM model. Front Inform Technol Electron Eng, 22(12):1641-1654.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2025 Journal of Zhejiang University-SCIENCE