Nonlinear Hammerstein System Identification: A Novel Application of Marine Predator Optimization Using the Key Term Separation Technique

Mehmood, Khizer; Chaudhary, Naveed Ishtiaq; Khan, Zeshan Aslam; Cheema, Khalid Mehmood; Raja, Muhammad Asif Zahoor; Milyani, Ahmad H.; Azhari, Abdullah Ahmed

doi:10.3390/math10224217

Cited by 21 publications

(10 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, the key term separation principle is introduced in the PSO to accurately estimate the actual parameters of the Hammerstein nonlinear system by avoiding the redundant parameters. [ 25 ] present marine predators based optimization heuristics for a parameter estimation of Hammerstein output error systems. The marine predator is a recently introduced swarm intelligence optimization approach that mimics the behavior of predators for catching prey through Brownian and Levy distributions for estimating the optimum communication between predator and prey.…”

Section: Related Workmentioning

confidence: 99%

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model

et al. 2023

Self Cite

View full text Add to dashboard Cite

The objective of this paper is to present a novel design of intelligent neuro-supervised networks (INSNs) in order to study the dynamics of a mathematical model for Parkinson’s disease illness (PDI), governed with three differential classes to represent the rhythms of brain electrical activity measurements at different locations in the cerebral cortex. The proposed INSNs are constructed by exploiting the knacks of multilayer structure neural networks back-propagated with the Levenberg–Marquardt (LM) and Bayesian regularization (BR) optimization approaches. The reference data for the grids of input and the target samples of INSNs were formulated with a reliable numerical solver via the Adams method for sundry scenarios of PDI models by way of variation of sensor locations in order to measure the impact of the rhythms of brain electrical activity. The designed INSNs for both backpropagation procedures were implemented on created datasets segmented arbitrarily into training, testing, and validation samples by optimization of mean squared error based fitness function. Comparison of outcomes on the basis of exhaustive simulations of proposed INSNs via both LM and BR methodologies was conducted with reference solutions of PDI models by means of learning curves on MSE, adaptive control parameters of algorithms, absolute error, histogram error plots, and regression index. The outcomes endorse the efficacy of both INSNs solvers for different scenarios in PDI models, but the accuracy of the BR-based method is relatively superior, albeit at the cost of slightly more computations.

show abstract

Section: Related Workmentioning

confidence: 99%

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…The main purpose of MTL is to share knowledge across multiple similar tasks in order to improve the performance of each individual task. A related concept is the swarm intelligence optimization algorithm, 36,37 which is often utilized for system identification. This algorithm simulates various group behaviors of creatures found in nature and utilizes mutual communication and cooperation among individuals in the group to achieve optimal parameter search and identification.…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed online multi‐task sparse identification for multiple systems with asynchronous updates

Zhang,

Luan,

Ding

et al. 2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Simultaneous online identification of the structure and parameters for multiple systems can use the multi‐task learning to share parameter sparse patterns across different identification tasks to improve the estimation accuracy. To reduce the communication and computational burden, improve the convergence rate and increase the reliability of the identification task, a distributed asynchronous multi‐task sparse identification algorithm is proposed. The alternating minimization algorithm is applied to design a distributed multi‐task loss function. Then by constructing the auxiliary matrix and transforming the sparse constraint on the parameter matrix to the constructed auxiliary matrix, the asynchronous parameter update and sparse model update can be realized. The algorithm uses a recursive form to complete online identification which is feasible to analyze. After proving the convergence of the identification algorithm, we present an example to support the theoretical analysis and show the advantages of the proposed algorithm.

show abstract

“…Parameter estimation plays an important role in system identification, which is the frontier of research in signal processing [ 1 ]. It is widely applied in various applications such as Hammerstein autoregressive system [ 2 ], water turbine [ 3 ], electrical machines [ 4 ], fuel cells [ 5 ], recurrent neural networks [ 6 ], health [ 7 ], Hammerstein–Wiener system [ 8 ], computer-aided design [ 9 ], renewable energy resources [ 10 ], honey production [ 11 ], Magnetorheological dampers [ 12 ], and smart grids [ 13 ]. Various techniques were proposed in the literature related to parameter estimation such as metaheuristics [ 14 ], fractional algorithms [ 15 ], least mean square [ 16 ], fuzzy logic [ 17 ], analytical methods [ 18 ], and machine learning [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

et al. 2023

Self Cite

View full text Add to dashboard Cite

In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels. The comparative performance analyses with standard counterparts indicate the worth of the ICGWO for ARX model identification, while the statistical analyses endorse the efficacy of the proposed chaotic scheme in terms of accuracy, robustness, and reliability.

show abstract

Nonlinear Hammerstein System Identification: A Novel Application of Marine Predator Optimization Using the Key Term Separation Technique

Cited by 21 publications

References 73 publications

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model

Distributed online multi‐task sparse identification for multiple systems with asynchronous updates

Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

Contact Info

Product

Resources

About