Novel computing paradigms for parameter estimation in Hammerstein controlled auto regressive auto regressive moving average systems

Mehmood, Ammara; Chaudhary, Naveed Ishtiaq; Zameer, Aneela; Raja, Muhammad Asif Zahoor

doi:10.1016/j.asoc.2019.03.052

Cited by 25 publications

(6 citation statements)

References 69 publications

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“…As future work, and because of the importance of the system identification and parameter estimation for describing stochastic references behavior, we consider extending this proposed method to other approaches and perspectives to look for an improvement in its description performance. Such as the Hammerstein mentioned in [30], which has been increasing in this specific area.…”

Section: Discussionmentioning

confidence: 97%

Adaptive Filtering Approach With Forgetting Factor for Stochastic Signals Applied to EEG

et al. 2020

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This paper presents a new stochastic adaptive estimation-identification technique for nonstationary systems. The proposed method enhances the initial results from an on average estimation, and its identification, through a generalized adaptable function based on the Exponential Forgetting Factor (EFF), and the Sliding Mode (SM) regarding the error identification. In this form, the presented process includes the function implementation in three stages-estimation, adaptive estimation, and adaptive estimationidentification, allowing us to observe the gradual convergence to a nonstationary reference signal. Simulations first introduce convergence level checks obtained from the estimation and identification of artificial signals. After that, the algorithm is applied for real references, considering the Electroencephalogram (EEG) signals taken from a public database, finding their internal nonstationary gains, indirectly. Finally, the results include a performance comparison between the proposed strategy concerning the Recursive Least Square (RLS), the Least Mean Square (LMS), and the Kalman Filter (KF).

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Section: Discussionmentioning

confidence: 97%

Adaptive Filtering Approach With Forgetting Factor for Stochastic Signals Applied to EEG

et al. 2020

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“…In the paper they hybrid the GA with the recursive least-squares (RLS) method and their proposed method shows a superior identification performance. In another study, the Hammerstein controlled auto regressive auto regressive moving average (HCARARMA) system is proposed and the parameter is estimated by using DE, GAs, pattern search (PS) and simulated annealing (SA) algorithms [43]. They show that the complexity measures for GAs are higher than the rest of the optimization mechanisms.…”

Section: Related Workmentioning

confidence: 99%

Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

2022

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Metaheuristics have been acknowledged as an effective solution for many difficult issues related to optimization. The metaheuristics, especially swarm’s intelligence and evolutionary computing algorithms, have gained popularity within a short time over the past two decades. Various metaheuristics algorithms are being introduced on an annual basis and applications that are more new are gradually being discovered. This paper presents a survey for the years 2011-2021 on multiple metaheuristics algorithms, particularly swarm and evolutionary algorithms, to identify a nonlinear block-oriented model called the Hammerstein model, mainly because such model has garnered much interest amidst researchers to identify nonlinear systems. Besides introducing a complete survey on the various population-based algorithms to identify the Hammerstein model, this paper also investigated some empirically verified actual process plants results. As such, this article serves as a guideline on the fundamentals of identifying nonlinear block-oriented models for new practitioners, apart from presenting a comprehensive summary of cutting-edge trends within the context of this topic area.

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“…Parameter identification is a highly studied problem due to its multiple fields of application. For example, Hammerstein controlled auto-regressive moving average systems and autonomous robot navigation in the state of charge estimation of lithium-ion batteries [1][2][3]. One of the studied systems for their parametric identification is the direct current motors due to their increasing use in high-demand applications [4].…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Parameter Identification of Motors Using Dynamic Response Relations

Rodríguez-Abreo

Rodríguez‐Reséndiz

Álvarez-Alvarado

et al. 2022

Sensors

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This article presents the use of the equations of the dynamic response to a step input in metaheuristic algorithm for the parametric estimation of a motor model. The model equations are analyzed, and the relations in steady-state and transient-state are used as delimiters in the search. These relations reduce the number of random parameters in algorithm search and reduce the iterations to find an acceptable result. The tests were implemented in two motors of known parameters to estimate the performance of the modifications in the algorithms. Tests were carried out with three algorithms (Gray Wolf Optimizer, Jaya Algorithm, and Cuckoo Search Algorithm) to prove that the benefits can be extended to various metaheuristics. The search parameters were also varied, and tests were developed with different iterations and populations. The results show an improvement for all the algorithms used, achieving the same error as the original method but with 10 to 50% fewer iterations.

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Novel computing paradigms for parameter estimation in Hammerstein controlled auto regressive auto regressive moving average systems

Cited by 25 publications

References 69 publications

Adaptive Filtering Approach With Forgetting Factor for Stochastic Signals Applied to EEG

Adaptive Filtering Approach With Forgetting Factor for Stochastic Signals Applied to EEG

Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

Metaheuristic Parameter Identification of Motors Using Dynamic Response Relations

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