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
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.
[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>