Identification of hysteresis models using real-coded genetic algorithms

Khasawneh, Hussam J.; Abo-Hammour, Zaer S.; Saaideh, Mohammad Al; Momani, Shaher

doi:10.1140/epjp/i2019-12883-7

Cited by 12 publications

(5 citation statements)

References 71 publications

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“…A related scientific and technical problem is the identification of the parameters of different hysteresis models [190][191][192][193]. Various methods for calculating the characteristics of the Preisach converter are used in [194][195][196][197][198][199].…”

Section: Technical Systemsmentioning

confidence: 99%

The Preisach model of hysteresis: fundamentals and applications

Semenov,

Borzunov,

Meleshenko

et al. 2024

Phys. Scr.

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The Preisach model is a well-known model of hysteresis in the modern nonlinear science. This paper provides an overview of works that are focusing on the study of dynamical systems from various areas (physics, economics, biology), where the Preisach model plays a key role in the formalization of hysteresis dependencies. Here we describe the input-output relations of the classical Preisach operator, its basic properties, methods of constructing the output using the demagnetization function formalism, a generalization of the classical Preisach operator for the case of vector input-output relations. Various generalizations of the model are described here in relation to systems containing ferromagnetic and ferroelectric materials. The main attention we pay to experimental works, where the Preisach model has been used for analytic description of the experimentally observed results. Also, we describe a wide range of the technical applications of the Preisach model in such fields as energy storage devices, systems under piezoelectric effect, models of systems with long-term memory. The properties of the Preisach operator in terms of reaction to stochastic external impacts are described and a generalization of the model for the case of the stochastic threshold numbers of its elementary components is given.

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Section: Technical Systemsmentioning

confidence: 99%

The Preisach model of hysteresis: fundamentals and applications

Semenov,

Borzunov,

Meleshenko

et al. 2024

Phys. Scr.

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“…In the recent literature, the importance of artificial intelligence in the field of computational biology is addressed. The tools, such as classification, networks and imaging tools, have been used to optimize the complex datasets [13,28,29].…”

Section: Machine Learning and Epidemiologymentioning

confidence: 99%

Forecasting the impact of environmental stresses on the frequent waves of COVID19

Abdel‐Salam

Sohail

et al. 2021

Nonlinear Dyn

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A novel approach to link the environmental stresses with the COVID-19 cases is adopted during this research. The time-dependent data are extracted from the online repositories that are freely available for knowledge and research. Since the time series data analysis is desired for the COVID-19 time-dependent frequent waves, here in this manuscript, we have developed a time series model with the aid of "nonlinear autoregressive network with exogenous inputs (NARX)" approach. The distribution of infectious agent-containing droplets from an infected person to an uninfected person is a common form of respiratory disease transmission. SARS-CoV-2 has mainly spread via short-range respiratory droplet transmission. Airborne transmission of SARS-CoV-2 seems to have occurred over long distances or times in unusual conditions; SARS-CoV-2 RNA was found in PM10 collected in Italy. This research shows that SARS-CoV-2 particles adsorbed to outdoor PM remained viable for a long time, given the epidemiology of outdoor

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“…This paper adopted real number coding [19][20][21][22], each gene was represented by a character set and composed of I (number of maintenance measures) strings (sub-genes), as shown in Figure 1, J1 represents the set of reinforcement measures, the numbers 1-6 represent the reinforcement measures, as shown in Formula 10; J2 represents the set of maintenance measures, the numbers 1-4 represent the maintenance measures, as shown in Formula 11.…”

Section: Individual Codingmentioning

confidence: 99%

Intelligent Optimization Algorithm for Maintenance Scheme Based on Life Cycle Cost

Liu¹,

Li²,

Wang³

et al. 2020

JESA

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In engineering practice, to make sure that a project can achieve safe operation while minimizing the overall cost during the whole life cycle, the supervisor of the project generally needs to make optimal decisions for the Life Cycle Cost (LCC) of the project. To this end, this paper adopted Genetic Algorithms (GA) and LCC theory to propose and implement a kind of optimization algorithms suitable for solving maintenance scheme problems. Combining with the selection of three actual maintenance scenarios of "take no maintenance measure/preventive maintenance measures only", "take major maintenance measures", and "take major maintenance measures and preventive maintenance measures", the proposed algorithm adopted real number coding to give optimization solutions from two perspectives of "control service life and calculate cost" and "control cost and calculate service life"; moreover, the paper conducted a comparative analysis on the maintenance schemes of reinforced concrete bridge decks using Matlab and verified the reliability and efficiency of the proposed algorithm. Keywords:life cycle cost (LCC), intelligent optimization, genetic algorithm (GA), optimal solution, maintenance scheme LCC ANALYSIS OF THE EXAMPLE PROJECT Description of the problemLCC analysis is the basis of engineering projects and scheme optimization, this paper took the reinforced concrete bridge deck as the research object, the initial reliability was set to be β=8.0, the degradation start time was set as T1=15, and Journal Européen des Systèmes Automatisés

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Identification of hysteresis models using real-coded genetic algorithms

Cited by 12 publications

References 71 publications

The Preisach model of hysteresis: fundamentals and applications

The Preisach model of hysteresis: fundamentals and applications

Forecasting the impact of environmental stresses on the frequent waves of COVID19

Intelligent Optimization Algorithm for Maintenance Scheme Based on Life Cycle Cost

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