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Relevant bibliographies by topics / Stochastic algorithms parameters identification

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Academic literature on the topic 'Stochastic algorithms parameters identification'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Stochastic algorithms parameters identification"

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Zhang, Ce, Xiangxiang Meng, and Yan Ji. "Parameter Estimation of Fractional Wiener Systems with the Application of Photovoltaic Cell Models." Mathematics 11, no. 13 (June 30, 2023): 2945. http://dx.doi.org/10.3390/math11132945.

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Fractional differential equations are used to construct mathematical models and can describe the characteristics of real systems. In this paper, the parameter estimation problem of a fractional Wiener system is studied by designing linear filters which can obtain smaller tunable parameters and maintain the stability of the parameters in any case. To improve the identification performance of the stochastic gradient algorithm, this paper derives two modified stochastic gradient algorithms for the fractional nonlinear Wiener systems with colored noise. By introducing the forgetting factor, a forgetting factor stochastic gradient algorithm is deduced to improve the convergence rate. To achieve more efficient and accurate algorithms, we propose a multi-innovation forgetting factor stochastic gradient algorithm by means of the multi-innovation theory, which expands the scalar innovation into the innovation vector. To test the developed algorithms, a fractional-order dynamic photovoltaic model is employed in the simulation, and the dynamic elements of this photovoltaic model are estimated using the modified algorithms. Concurrently, a numerical example is given, and the simulation results verify the feasibility and effectiveness of the proposed procedures.

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Ji, Yuejiang, and Lixin Lv. "Two Identification Methods for a Nonlinear Membership Function." Complexity 2021 (April 30, 2021): 1–7. http://dx.doi.org/10.1155/2021/5515888.

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This paper proposes two parameter identification methods for a nonlinear membership function. An equation converted method is introduced to turn the nonlinear function into a concise model. Then a stochastic gradient algorithm and a gradient-based iterative algorithm are provided to estimate the unknown parameters of the nonlinear function. The numerical example shows that the proposed algorithms are effective.

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Hu, Huiyi, Xiao Yongsong, and Rui Ding. "Multi-Innovation Stochastic Gradient Identification Algorithm for Hammerstein Controlled Autoregressive Autoregressive Systems Based on the Key Term Separation Principle and on the Model Decomposition." Journal of Applied Mathematics 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/596141.

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An input nonlinear system is decomposed into two subsystems, one including the parameters of the system model and the other including the parameters of the noise model, and a multi-innovation stochastic gradient algorithm is presented for Hammerstein controlled autoregressive autoregressive (H-CARAR) systems based on the key term separation principle and on the model decomposition, in order to improve the convergence speed of the stochastic gradient algorithm. The key term separation principle can simplify the identification model of the input nonlinear system, and the decomposition technique can enhance computational efficiencies of identification algorithms. The simulation results show that the proposed algorithm is effective for estimating the parameters of IN-CARAR systems.

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Olama, Mohammed M., Kiran K. Jaladhi, Seddik M. Djouadi, and Charalambos D. Charalambous. "Recursive Estimation and Identification of Time-Varying Long-Term Fading Channels." Research Letters in Signal Processing 2007 (2007): 1–5. http://dx.doi.org/10.1155/2007/17206.

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This paper is concerned with modeling of time-varying wireless long-term fading channels, parameter estimation, and identification from received signal strength data. Wireless channels are represented by stochastic differential equations, whose parameters and state variables are estimated using the expectation maximization algorithm and Kalman filtering, respectively. The latter are carried out solely from received signal strength data. These algorithms estimate the channel path loss and identify the channel parameters recursively. Numerical results showing the viability of the proposed channel estimation and identification algorithms are presented.

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Ma, Ping, and Lei Wang. "Partially Coupled Stochastic Gradient Estimation for Multivariate Equation-Error Systems." Mathematics 10, no. 16 (August 16, 2022): 2955. http://dx.doi.org/10.3390/math10162955.

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This paper researches the identification problem for the unknown parameters of the multivariate equation-error autoregressive systems. Firstly, the original identification model is decomposed into several sub-identification models according to the number of system outputs. Then, based on the characteristic that the information vector and the parameter vector are common among the sub-identification models, the coupling identification concept is used to propose a partially coupled generalized stochastic gradient algorithm. Furthermore, by expanding the scalar innovation of each subsystem model to the innovation vector, a partially coupled multi-innovation generalized stochastic gradient algorithm is proposed. Finally, the numerical simulations indicate that the proposed algorithms are effective and have good parameter estimation performances.

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Tsyganov, Andrey, and Yulia Tsyganova. "SVD-Based Identification of Parameters of the Discrete-Time Stochastic Systems Models with Multiplicative and Additive Noises Using Metaheuristic Optimization." Mathematics 11, no. 20 (October 15, 2023): 4292. http://dx.doi.org/10.3390/math11204292.

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The paper addresses a parameter identification problem for discrete-time stochastic systems models with multiplicative and additive noises. Stochastic systems with additive and multiplicative noises are considered when solving many practical problems related to the processing of measurements information. The purpose of this work is to develop a numerically stable gradient-free instrumental method for solving the parameter identification problems for a class of mathematical models described by discrete-time linear stochastic systems with multiplicative and additive noises on the basis of metaheuristic optimization and singular value decomposition. We construct an identification criterion in the form of the negative log-likelihood function based on the values calculated by the newly proposed SVD-based Kalman-type filtering algorithm, taking into account the multiplicative noises in the equations of the state and measurements. Metaheuristic optimization algorithms such as the GA (genetic algorithm) and SA (simulated annealing) are used to minimize the identification criterion. Numerical experiments confirm the validity of the proposed method and its numerical stability compared with the usage of the conventional Kalman-type filtering algorithm.

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Kovacevic, Ivana, Branko Kovacevic, and Zeljko Djurovic. "On strong consistency of a class of recursive stochastic Newton-Raphson type algorithms with application to robust linear dynamic system identification." Facta universitatis - series: Electronics and Energetics 21, no. 1 (2008): 1–21. http://dx.doi.org/10.2298/fuee0801001k.

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The recursive stochastic algorithms for estimating the parameters of linear discrete-time dynamic systems in the presence of disturbance uncertainty has been considered in the paper. Problems related to the construction of min-max optimal recursive algorithms are demonstrated. In addition, the robustness of the proposed algorithms has been addressed. Since the min-max optimal solution cannot be achieved in practice, an approximate optimal solution based on a recursive stochastic Newton-Raphson type procedure is suggested. The convergence of the proposed practically applicable robustified recursive algorithm is established theoretically using the martingale theory. Both theoretical and experimental analysis related to the practical robustness of the proposed algorithm are also included. .

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Maitre, Julien, Sébastien Gaboury, Bruno Bouchard, and Abdenour Bouzouane. "A Black-Box Model for Estimation of the Induction Machine Parameters Based on Stochastic Algorithms." International Journal of Monitoring and Surveillance Technologies Research 3, no. 3 (July 2015): 44–67. http://dx.doi.org/10.4018/ijmstr.2015070103.

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Knowledge on asynchronous machine parameters (resistances, inductances…) has become necessary for the manufacturing industry in the interest of optimizing performances in a production system (roll-to-roll processing, wind generator…). Indeed, accurate values of this machine allow improving control of the torque, speed and position, managing power consumption in the best way possible, and predicting induction machine failures with great effectiveness. In these regards, the authors of this paper propose a black-box modeling for a powerful identification of asynchronous machine parameters relying on stochastic research algorithms. The algorithms used for the estimation process are a single objective genetic algorithm, the well-known NSGA II and the new ?-NSGA III (multi-objective genetic algorithms). Results provided by those show that the best estimation of asynchronous machines parameters is given by ?-NSGA III. In addition, this outcome is confirmed by performing the identification process on three different induction machines.

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Hsu, Geesern, Andrew E. Yagle, Kenneth C. Ludema, and Joel A. Levitt. "Modeling and Identification of Lubricated Polymer Friction Dynamics." Journal of Dynamic Systems, Measurement, and Control 122, no. 1 (October 11, 1996): 78–88. http://dx.doi.org/10.1115/1.482431.

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A systematic approach is proposed to model the dynamics of lubricated polymer friction. It starts with the development of a physical model to describe the fundamental mechanisms of the friction. The physical model then serves as the basic structure for the development of a complex model able to capture a wider spectrum of the deterministic and stochastic dynamics of friction. To assess the accuracy of the complex model, two estimation algorithms are formulated to estimate the unknown parameters in the model and to test the model against experimental data. One algorithm is based on the maximum likelihood principle to estimate the constant parameters for stationary friction dynamics, and the other based on the extended Kalman filter to estimate the time-varying parameters for nonstationary friction dynamics. The model and the algorithms are all validated through experiments. [S0022-0434(00)00601-8]

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Krasheninnikov, Viktor R., Yuliya E. Kuvayskova, Olga E. Malenova, and Aleksey Y. Subbotin. "PSEUDOGRADIENT ALGORITHM FOR IDENTIFICATION OF DOUBLY STOCHASTIC CYLINDRICAL IMAGE." Автоматизация процессов управления 2, no. 64 (2021): 56–65. http://dx.doi.org/10.35752/1991-2927-2021-2-64-56-65.

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Nowadays image processing problems are becoming increasingly important due to development of the aerospace Earth monitoring systems, radio and sonar systems, medical devices for early diagnosis, etc. However, the most of the image processing works deals with images defined on rectangular two-dimensional grids or grids of higher dimension. In some practical situations images are set on a cylinder, for example images of pipeline sections, blood vessels, rotary parts, etc. The peculiarity of the domain for specifying such images requires its consideration in their models and processing algorithms. The article deals with autoregressive models of cylindrical images and gives some expressions of the correlation function depending on the autoregression parameters are given. To represent heterogeneous images with random heterogeneities, ‘doubly stochastic’ models are used in which one or more images control the parameters of resulted image. The spiral scan of a cylindrical image can be considered as a quasiperiodic process due to the correlation of image rows. The article proposes the pseudogradient algorithms for the modal identification. The statistical modeling proves these algorithms give good model identification.

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Dissertations / Theses on the topic "Stochastic algorithms parameters identification"

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Larsson, Erik. "Identification of stochastic continuous-time systems : algorithms, irregular sampling and Cramér-Rao bounds /." Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3944.

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Koenig, Guillaume. "Par vagues et marées : étude de la circulation hydrodynamique d’un lagon étroit de Nouvelle-Calédonie et identification des conditions aux bords à l’aide d’un algorithme stochastique." Electronic Thesis or Diss., Aix-Marseille, 2021. http://www.theses.fr/2021AIXM0533.

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Dans cette thèse, j’ai étudié l’hydrodynamique du lagon de Ouano en Nouvelle-Calédonie. Pour ce faire, j’ai implémenté un nouvel algorithme d’identification de paramètres. Le déferlement des vagues sur la barrière corallienne et les marées dominent l’hydrodynamique du lagon de Ouano. Je voulais évaluer leur impact relatif sur l’échange d’eau avec l’océan. Plusieurs études ont été menées dans le lagon auparavant. Je me base sur leur résultats pour la circulation et les outils de modélisations qu’elles ont mis en place dans ma thèse. Notamment, je réutilise le modèle CROCO ( Coastal Regional OceanCOmmunity) de C. Chevalier. J’utilise aussi des données récoltées dans le lagon en2016. Malgré ces travaux préalables, il reste des incertitudes sur la quantité d’eau amenée dans le lagon par le déferlement des vagues et la marée. De plus, la paramétrisation du déferlement, de la friction sur le récif et les conditions aux bords de marées ont incertaines dans le modèle numérique. Pour améliorer ces paramétrisations, ou même d’autres paramètres, j’ai implémenté et testé un nouvel outil. Cet outil était un algorithme stochastique d’identification de paramètres, l’algorithme Simultaneous Pertubations Stochastic Approximations(SPSA).Nous avons d’abord testé différentes versions de l’algorithme dans des environnements contrôlés, et notamment avec un modèle de turbulence unidimensionnel. J’ai ensuite utilisé cet algorithme pour identifier des conditions aux bords avec un modèle tidal linéaire du lagon de Ouano. Enfin, j’ai utilisé l’algorithme pour étudier l’impact du déferlement des vagues sur les courants mesurés comme des courants de marée dans le lagon de Ouano
In this thesis, I have studied the hydrodynamics of the Ouano coral lagoon in NewCaledonia and implemented a novel parameter identification algorithm to do so.Wave-breaking and tides dominate the Ouano lagoon; I wanted to evaluate theirimpact on the lagoon flushing.Several studies have been done in the lagoon before. I rely on both their findings forthe circulation and their tools for the modeling, namely the CROCO ( Coastal RegionalOcean COmmunity model) of C. Chevalier. I also have used data collected in 2016 inthe lagoon. However, some uncertainties remained on the amount of water broughtby the tides and the wave-breaking in the lagoon. Also, the parametrization of thewave-breaking friction coefficient and the tidal boundary conditions in the numericalmodel was uncertain.I implemented and tested a tool to improve those parametrizations or other modelparameters. This tool was a stochastic parameter identification algorithm, the Simul-taneous Perturbations Stochastic Approximations (SPSA) algorithm.We first tested different variants of the algorithm in a controlled environment andwith a 1-D turbulence model. Then I have used this algorithm to identify boundaryconditions with a linear tidal model of the Ouano lagoon. Finally, I have used thealgorithm to study the impact of the wave-breaking on the measurement of tides inthe Ouano

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Jenča, Pavol. "Identifikace parametrů elektrických motorů metodou podprostorů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219678.

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The electrical motors parameters identification is solved in this master’s thesis using subspace based methods. Electrical motors are simulated in Matlab/Simulink interactive environment, specifically permanent magnet DC motor and permanent magnet synchronous motor. Identification is developed in Matlab interactive environment. Different types of subspace algorithms are used for the estimation of parameters. Results of subspace parameters estimation are compared with least squares parameters estimation. The thesis describes subspace method, types of subspace algorithms, used electrical motors, nonlinear approach of identification and comparation of parameters identification.

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Debonos, Andreas A. "Estimation of non-linear ship roll parameters using stochastic identification techniques." Thesis, University of Sussex, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295784.

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Alamyal, Mohamoud Omran A. "Evaluation of stochastic optimisation algorithms for induction machine winding fault identification." Thesis, University of Newcastle upon Tyne, 2013. http://hdl.handle.net/10443/1937.

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This thesis is concerned with parameters identification and winding fault detection in induction motors using three different stochastic optimisation algorithms, namely genetic algorithm (GA), tabu search (TS) and simulated annealing (SA). Although induction motors are highly reliable, require low maintenance and have relatively high efficiency, they are subject to many electrical and mechanical types of faults. Undetected faults can lead to serious machine failures. Fault identification is, therefore, essential in order to detect and diagnose potential failures in electrical motors. Conventional methods of fault detection usually involve embedding sensors in the machines, but these are very expensive. The condition monitoring technique proposed in this thesis flags the presence of a winding fault and provides information about its nature and location by using an optimisation stochastic algorithm in conjunction with measured time domain voltage, stator current data and rotor speed data. This technique requires a mathematical ABCabc model of the three-phase induction motor. The performance of the three stochastic search methods is evaluated in this thesis for their use to identify open-circuit faults in the stator and rotor windings of a three-phase induction motor. The proposed fault detection technique is validated through the use of experimental data collected under steady-state operating conditions. Time domain terminal voltages and the rotor speed are used as input data for the induction motor model while the outputs are the calculated stator currents. These calculated currents are compared to the measured currents to produce a set of current errors that are integrated and summed to give an overall error function. Fault identification is achieved by adjusting the model parameters off-line using the stochastic search method to minimise this error function. The estimate values for the winding parameters give the best possible match between the performance of the faulty experimental machine and its mathematical ABCabc model. These estimates of the values of the motor winding parameters are used in the detection of the development of faults by identifying both the location and the nature of the winding fault. The effectiveness of the three stochastic methods to identify stator and rotor winding faults are compared in terms of the required computation resources and their success rates in converging to a solution.

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Zhou, Haiyan. "Stochastic Inverse Methods to Identify non-Gaussian Model Parameters in Heterogeneous Aquifers." Doctoral thesis, Universitat Politècnica de València, 2011. http://hdl.handle.net/10251/12267.

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La modelación numérica del flujo de agua subterránea y del transporte de masa se está convirtiendo en un criterio de referencia en la actualidad para la evaluación de recursos hídricos y la protección del medio ambiente. Para que las predicciones de los modelos sean fiables, estos deben de estar lo más próximo a la realidad que sea posible. Esta proximidad se adquiere con los métodos inversos, que persiguen la integración de los parámetros medidos y de los estados del sistema observados en la caracterización del acuífero. Se han propuesto varios métodos para resolver el problema inverso en las últimas décadas que se discuten en la tesis. El punto principal de esta tesis es proponer dos métodos inversos estocásticos para la estimación de los parámetros del modelo, cuando estos no se puede describir con una distribución gausiana, por ejemplo, las conductividades hidráulicas mediante la integración de observaciones del estado del sistema, que, en general, tendrán una relación no lineal con los parámetros, por ejemplo, las alturas piezométricas. El primer método es el filtro de Kalman de conjuntos con transformación normal (NS-EnKF) construido sobre la base del filtro de Kalman de conjuntos estándar (EnKF). El EnKF es muy utilizado como una técnica de asimilación de datos en tiempo real debido a sus ventajas, como son la eficiencia y la capacidad de cómputo para evaluar la incertidumbre del modelo. Sin embargo, se sabe que este filtro sólo trabaja de manera óptima cuándo los parámetros del modelo y las variables de estado siguen distribuciones multigausianas. Para ampliar la aplicación del EnKF a vectores de estado no gausianos, tales como los de los acuíferos en formaciones fluvio-deltaicas, el NSEnKF propone aplicar una transformación gausiana univariada. El vector de estado aumentado formado por los parámetros del modelo y las variables de estado se transforman en variables con una distribución marginal gausiana.
Zhou ., H. (2011). Stochastic Inverse Methods to Identify non-Gaussian Model Parameters in Heterogeneous Aquifers [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12267
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Dong, Wei. "Identification of Electrical Parameters in A Power Network Using Genetic Algorithms and Transient Measurements." Thesis, University of Nottingham, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.523043.

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van, Wyk Hans-Werner. "A Variational Approach to Estimating Uncertain Parameters in Elliptic Systems." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/27635.

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As simulation plays an increasingly central role in modern science and engineering research, by supplementing experiments, aiding in the prototyping of engineering systems or informing decisions on safety and reliability, the need to quantify uncertainty in model outputs due to uncertainties in the model parameters becomes critical. However, the statistical characterization of the model parameters is rarely known. In this thesis, we propose a variational approach to solve the stochastic inverse problem of obtaining a statistical description of the diffusion coefficient in an elliptic partial differential equation, based noisy measurements of the model output. We formulate the parameter identification problem as an infinite dimensional constrained optimization problem for which we establish existence of minimizers as well as first order necessary conditions. A spectral approximation of the uncertain observations (via a truncated Karhunen-Loeve expansion) allows us to estimate the infinite dimensional problem by a smooth, albeit high dimensional, deterministic optimization problem, the so-called 'finite noise' problem, in the space of functions with bounded mixed derivatives. We prove convergence of 'finite noise' minimizers to the appropriate infinite dimensional ones, and devise a gradient based, as well as a sampling based strategy for locating these numerically. Lastly, we illustrate our methods by means of numerical examples.
Ph. D.

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Harth, Tobias [Verfasser]. "Identification of Material Parameters for Inelastic Constitutive Models : Stochastic Simulation and Design of Experiments / Tobias Harth." Aachen : Shaker, 2003. http://d-nb.info/1179036204/34.

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Wong, king-fung, and 黃景峰. "Non-coding RNA identification along genome." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B4581949X.

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Books on the topic "Stochastic algorithms parameters identification"

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System identification with quantized observations. Boston: Birkhäuser, 2010.

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R, Kumar. A novel multistage estimation of the signal parameters of a possibly data-modulated sinusoid under very high dynamics. Pasadena, Calif: National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1989.

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Kamenskaya, Valentina, and Leonid Tomanov. The fractal-chaotic properties of cognitive processes: age. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1053569.

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In the monograph the literature information about the nature of stochastic processes and their participation in the work of the brain and human behavior. Established that the real cognitive processes and mental functions associated with the procedural side of external events and the stochastic properties of the internal dynamics of brain systems in the form of fluctuations of their parameters, including cardiac rhythm generation and sensorimotor reactions. Experimentally proved that the dynamics of the measured physiological processes is in the range from chaotic regime to a weakly deterministic — fractal mode. Fractal mode determines the maximum order and organization homeostasis of cognitive processes and States, as well as high adaptive ability of the body systems with fractal properties. The fractal-chaotic dynamics is a useful quality to examine the actual physiological and psychological systems - a unique numerical identification of the order and randomness of the processes through calculation of fractal indices. The monograph represents the results of many years of experimental studies of the reflection properties of stochastic sensorimotor reactions, as well as stochastic properties of heart rate in children, Teens and adults in the age aspect in the speech activity and the perception of different kinds of music with its own frequency-spectral structure. Designed for undergraduates, graduate students and researchers that perform research and development on cognitive psychology and neuroscience.

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Farooqi, Zarreen H. H. Identification of stochastic systems with random parameters with particular reference to the recirculating lymphocytes in the immune system. 1986.

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Larsson, Erik. Identification of Stochastic Continuous-Time Systems: Algorithms, Irregular Sampling & Cramer-Rao Bounds (Uppsala Dissertations from the Faculty of Science & Technology, 52). Uppsala Universitet, 2003.

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Zhang, Ji-Feng, Le Yi Wang, and G. George Yin. System Identification with Quantized Observations. Birkhäuser, 2010.

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Leondes, Cornelius T. Control and Dynamic Systems: Advances in Theory and Applications : Advances in Algorithms and Computational Techniques in Dynamic Systems Control, P (Control and Dynamic Systems). Academic Press, 1989.

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Allen, Michael P., and Dominic J. Tildesley. Molecular dynamics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0003.

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This chapter introduces the classical equations of motion for a system of molecules, and describes their solution by stable, accurate, time-stepping algorithms. Simple atomic systems, rigid molecules, and flexible molecules with and without constraints, are treated, with examples of program code. Quaternions are introduced as useful parameters for solving the rigid-body equations of motion of molecules. A simple example of a multiple timestep algorithm is given, and there is a brief summary of event-driven (hard-particle) dynamics. Examples of constant-temperature molecular dynamics using stochastic and deterministic methods are presented, and the corresponding constant-pressure molecular dynamics methods for fixed and variable box-shape are described. The molecular dynamics method is extended to the treatment of polarizable systems, and dynamical simulation of the grand canonical ensemble is mentioned.

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Book chapters on the topic "Stochastic algorithms parameters identification"

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Al-Ani, Tarik, and Yskander Hamam. "Parameters identification of a time-varying stochastic dynamic systems using Viterbi algorithm." In System Modelling and Optimization, 567–73. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-0-387-34897-1_69.

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Benveniste, Albert, Michel Métivier, and Pierre Priouret. "Tracking Non-Stationary Parameters." In Adaptive Algorithms and Stochastic Approximations, 120–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75894-2_5.

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Guo, L., H. F. Chen, and J. F. Zhang. "Identification of Stochastic Time-Varying Parameters." In The IMA Volumes in Mathematics and its Applications, 211–23. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4613-9296-5_12.

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Cao, Yi, Yuehui Chen, and Yaou Zhao. "Stochastic System Identification by Evolutionary Algorithms." In Bio-Inspired Computing and Applications, 247–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24553-4_34.

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Boutalis, Yiannis, Dimitrios Theodoridis, Theodore Kottas, and Manolis A. Christodoulou. "Adaptive Estimation Algorithms of FCN Parameters." In System Identification and Adaptive Control, 215–49. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06364-5_9.

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Wang, Wei. "Generalized Extended Stochastic Gradient Algorithm Implemented Parameter Identification for Complex Multivariable-Systems." In Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019), 663–73. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0474-7_62.

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Bellizzi, S., and R. Bouc. "Identification of the Hysteresis Parameters of a Nonlinear Vehicle Suspension Under Random Excitation." In Nonlinear Stochastic Dynamic Engineering Systems, 467–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83334-2_34.

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Cascone, Dario, Giacomo Navarra, Maria Oliva, and Francesco Lo Iacono. "Influence of User-Defined Parameters Using Stochastic Subspace Identification (SSI)." In Lecture Notes in Mechanical Engineering, 1567–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41057-5_127.

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Harth, Tobias, Jürgen Lehn, and Franz Gustav Kollmann. "Identification of Material Parameters for Inelastic Constitutive Models: Stochastic Simulation." In Deformation and Failure in Metallic Materials, 139–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36564-8_6.

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de la Higuera, Colin, and Franck Thollard. "Identification in the Limit with Probability One of Stochastic Deterministic Finite Automata." In Grammatical Inference: Algorithms and Applications, 141–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-45257-7_12.

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Conference papers on the topic "Stochastic algorithms parameters identification"

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Chernyshov, Kirill R. "Extended stochastic approximation algorithms for systems parameters identification." In IEEE EUROCON 2009 (EUROCON). IEEE, 2009. http://dx.doi.org/10.1109/eurcon.2009.5167742.

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Wang, Chunlin, Torodd Skjerve Nord, and Guoyuan Li. "Automated Modal Parameters Identification During Ice-Structure Interactions." In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-81075.

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Abstract Offshore structures are prone to damage caused by ice-induced vibrations. It is presently unknown to what extent different ice conditions change the properties of the structure, such as natural frequency, damping ratio, and mode shape. Understanding the dynamic interaction between ice and structures are important for the operational ability of offshore structures. In this study, the covariance-driven stochastic subspace identification algorithm (SSI-cov) is introduced to identify modal parameters of a scale-model structure during ice-structure interactions. In order to reduce the number of user interactions and inherent bias to the identified modal parameters, we therefore introduce an automated parameter identification approach. First, SSI-cov is used to obtain poles that describe the information: damping ratio, mode shape, etc. After that, a stable criterion is used to pick up stable poles. Finally, Hierarchical clustering is used to cluster poles to identify the natural frequency. The proposed method is able to reduce the many user-intervenes and enables efficient automatic parameter identification. The results show that Hierarchical clustering can render more successful identifications than the slack value-based method among different ice speeds. The results also show changes in the system frequencies for different ice conditions.

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Wade, S. "Comparison of stochastic and deterministic parameter identification algorithms for indirect vector control." In IEE Colloquium on Vector Control and Direct Torque Control of Induction Motors. IEE, 1995. http://dx.doi.org/10.1049/ic:19951109.

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Houili, Rabiaa, Mohamed Yacine Hammoudi, Abir Betka, and Abdenacer Titaouine. "Stochastic optimization algorithms for parameter identification of three phase induction motors with experimental verification." In 2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS). IEEE, 2023. http://dx.doi.org/10.1109/icaeccs56710.2023.10104526.

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Wang, S. Q., Y. T. Zhang, and Y. X. Feng. "Comparative Study of Output-Based Modal Identification Methods Using Measured Signals From an Offshore Platform." In ASME 2010 29th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/omae2010-20771.

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Research on vibration based damage identification using changes of modal parameters has been expanding rapidly over the last decades. And modal parameter identification methods are essentially important since these parameters directly affect the damage detection results. Many modal identification methods have been proposed and further verified based on numerical and laboratory data. However, few papers are reported on comparative investigation of several output-only modal identification methods, especially based on measured signals from offshore platform in service. The main objective of the present paper is to investigate the effectiveness and applicability of several output-only modal identification methods, including Ibrahim Time Domain(ITD), Eigensystem Realization Algorithm(ERA), Auto-Regressive Moving Average method(ARMA) and Stochastic Subspace Identification(SSI) methods. First the four modal identification methods are briefly reviewed. Field measurements are conducted and vibration signals are recorded for modal identification. The effectiveness is compared for the four identification methods. Useful results could be obtained.

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Book, Joel M., and Samuel F. Asokanthan. "Modal Characterization of MEMS Switches via Output Only and Input/Output Identification Methods." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48888.

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MEMS devices typically have moving or oscillating mechanical parts, and characterization of their dynamics, including their modal parameters, is highly desirable. This paper is concerned with experimental implementation of a Stochastic Subspace Identification (SSI) algorithm as well a base excitation based identification algorithm for experimental modal analysis of a micro-cantilever switch. A white noise signal applied to the built-in electrostatic actuator in the switches excited a response measured using microscanning Laser Doppler Vibrometry (LDV). In the case of identification via the SSI, only the output response was used while the base excitation based algorithm employed the input and the output signals. The modal parameters found using MACEC matched well with those predicted by theory, and the results obtained via the two experimental identification approaches are in good agreement, thus providing confidence in using the SSI approach for experimental modal analysis of MEMS structures.

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Yang, Wenlong, Lei Li, Qiang Fu, Yao Teng, Shuqing Wang, and Fushun Liu. "Identify Modal Parameters of a Real Offshore Platform From the Response Excited by Natural Ice Loading." In ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54821.

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Experimental modal analysis (EMA) is widely implemented to obtain the modal parameters of an offshore platform, which is crucial to many practical engineering issues, such as vibration control, finite element model updating and structural health monitoring. Traditionally, modal parameters are identified from the information of both the input excitation and output response. However, as the size of offshore platforms becomes huger, imposing artificial excitation is usually time-consuming, expensive, sophisticated and even impossible. To address this problem, a preferred solution is operational modal analysis (OMA), which means the modal testing and analysis for a structure is in its operational condition subjected to natural excitation with output-only measurements. This paper investigate the applicability of utilizing response from natural ice loading for operational modal analysis of real offshore platforms. The test platform is the JZ20-2MUQ Jacket platform located in the Bohai Bay, China. A field experiment is carried out in winter season, when the platform is excited by floating ices. An accelerometer is installed on a leg and two segments of acceleration response are employed for identifying the modal parameters. In the modal parameter identification, specifically applied is the data-driven stochastic sub-space identification (SSI-data) method. It is one of the most advanced methods based on the first-order stochastic model and the QR algorithm for computing the structural eigenvalues. To distinguish the structural modal information, stability diagrams are constructed by identifying parametric models of increasing order. Observing the stability diagrams, the modal frequencies and damping ratios of four structural modes can be successfully identified from both segments. The estimated information from both segments are almost identical, which demonstrates the identification is trustworthy. Besides, the stability diagrams from SSI-data method are very clean, and the poles associated with structural modes can become stabilized at very low model order. The research in this paper is meaningful for the platforms serving in cold regions, where the ices could be widespread. Utilizing the response from natural ice loading for modal parameter identification would be efficient and cost-effective.

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Yin, Hao, He Xu, Yuhan Zhao, and Feng Sun. "Fault Diagnosis of Control Valve Based on Fusion of Deep Learning and Elastic Weight Consolidation." In BATH/ASME 2022 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/fpmc2022-89359.

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Abstract Deep neural network learning is a commonly used method for fault diagnosis of the control valve. However, the catastrophic forgetting problem of deep learning in multi-task affects the fault diagnosis accuracy. Moreover, the traditional training model can be improved by using parameter constraint control or adding a few parameters, but it has many limitations. Therefore, this paper proposed a fusion of elastic weight consolidation algorithm and residual shrinkage network method, sharing common feature layers. According to the weight of the same or similar parameters of the previous task, the correct solution of the current task could be obtained, and the forgetting degree of the previous task could be reduced. It improved the generalization ability of the training model. The control valve data were collected and compared with the stochastic gradient descent algorithm in different valve openings. The results indicate that this method has a high accuracy for the condition identification of the control valve. This method can effectively alleviate the problem of the catastrophic forgetting of deep learning in multi-task identification of control valve.

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Yu, Shi Miao, Youngmok Ko, Han Hu, Jun Seo, and Amy M. Bilton. "Optimization and System Identification of a Variable Pico-Scale Hydro Turbine for Pressure Regulation." In ASME 2020 Power Conference collocated with the 2020 International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/power2020-16902.

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Abstract Recent studies from the European Commission estimate that more than 20% of global energy is consumed by pumping systems. Significant research has focused on increasing pump efficiency to lower energy consumption; however, few have looked at the energy lost in use of pressure regulating devices (PRDs). This paper proposes a novel pico-scale hydro turbine that could effectively replace PRDs and generate power while regulating pressure. The proposed hydro turbine has an outer diameter of 4″ and a total length of 5.4″. The turbine uses 14 rotating guide vanes and is attached to a generator with a variable load. To maximize power recovery and pressure control range of the turbine, a non-dominated sorting genetic algorithm was used for multi-objective geometry optimization. Then, to build a dynamic model for control system design, parameter identification was conducted using a Gaussian process surrogate model and stochastic search algorithms: particle swarm optimization and genetic algorithm. The optimized turbine showed good agreement between simulated and experimental results and achieved a power output of 120 W, pressure drop range of 6 to 27 psi, and maximum hydraulic efficiency of 75% at the rated flow rate of 27 GPM. The optimized turbine shows the potential of pico-turbines for pressure regulation.

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Carassale, Luigi, Michela Marrè-Brunenghi, and Stefano Patrone. "Modal Identification of Dynamically Coupled Bladed Disks in Run-Up Tests." In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-57251.

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The spin test is a standard industrial practice employed for the qualification of rotor blades and disks. The expected results are the modal properties of blades and assemblages at different rotation velocities. If a significant dynamic coupling among the blades exists, global vibration modes appear, reflecting into a set of closely spaced natural frequencies for each mode family. In case of perfectly-tuned bladed disks, the circumferential structure of the mode shapes is known and can be exploited during the identification process so that traditional single-dof models may be applied. On the contrary, the mode irregularities produced by mistuning prevents the use of single-dof models requiring the development of more sophisticated approaches. In this work, we propose a multi-dof identification technique organized as follow: 1) the FRF of the bladed disk in the neighborhood of a resonance crossing is identified by the wavelet transform of the measured response; 2) the modal parameters of the system are estimated using a mixed stochastic-deterministic subspace algorithm formulated in the frequency domain. The procedure is validated using a realistic numerical simulation.

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Reports on the topic "Stochastic algorithms parameters identification"

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Heeringa, Brent, and Tim Oates. Two Algorithms for Learning the Parameters of Stochastic Context-Free Grammars. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada459920.

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Kuropiatnyk, D. I. Actuality of the problem of parametric identification of a mathematical model. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2885.

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The purpose of the article is to study the possibilities of increasing the efficiency of a mathematical model by identifying the parameters of an object. A key factor for parametrization can be called the consideration of properties of the values of the model at a specific time point, which allows a deeper analysis of data dependencies and correlation between them. However, such a technique does not always work, because in advance it is impossible to predict that the parameters can be substantially optimized. In addition, it is necessary to take into account the fact that minimization reduces the values of parameters without taking into account their real physical properties. The correctness of the final values will be based on dynamically selected parameters, which allows you to modify the terms of use of the system in real time. In the development process, the values of experimentally obtained data with the model are compared, which allows you to understand the accuracy of minimization. When choosing the most relevant parameters, various minimization functions are used, which provides an opportunity to cover a wide range of theoretical initial situations. Verification of the correctness of the decision is carried out with the help of a quality function, which can identify the accuracy and correctness of the optimized parameters. It is possible to choose different types of functional quality, depending on the characteristics of the initial data. The presence of such tools during parametrization allows for varied analysis of the model, testing it on various algorithms, data volumes and conditions of guaranteed convergence of functional methods.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.

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Miles, Gaines E., Yael Edan, F. Tom Turpin, Avshalom Grinstein, Thomas N. Jordan, Amots Hetzroni, Stephen C. Weller, Marvin M. Schreiber, and Okan K. Ersoy. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, August 1995. http://dx.doi.org/10.32747/1995.7570567.bard.

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In this work multispectral reflectance images are used in conjunction with a neural network classifier for the purpose of detecting and classifying weeds under real field conditions. Multispectral reflectance images which contained different combinations of weeds and crops were taken under actual field conditions. This multispectral reflectance information was used to develop algorithms that could segment the plants from the background as well as classify them into weeds or crops. In order to segment the plants from the background the multispectrial reflectance of plants and background were studied and a relationship was derived. It was found that using a ratio of two wavelenght reflectance images (750nm and 670nm) it was possible to segment the plants from the background. Once ths was accomplished it was then possible to classify the segmented images into weed or crop by use of the neural network. The neural network developed for this work is a modification of the standard learning vector quantization algorithm. This neural network was modified by replacing the time-varying adaptation gain with a constant adaptation gain and a binary reinforcement function. This improved accuracy and training time as well as introducing several new properties such as hill climbing and momentum addition. The network was trained and tested with different wavelength combinations in order to find the best results. Finally, the results of the classifier were evaluated using a pixel based method and a block based method. In the pixel based method every single pixel is evaluated to test whether it was classified correctly or not and the best weed classification results were 81% and its associated crop classification accuracy is 57%. In the block based classification method, the image was divided into blocks and each block was evaluated to determine whether they contained weeds or not. Different block sizes and thesholds were tested. The best results for this method were 97% for a block size of 8 inches and a pixel threshold of 60. A simulation model was developed to 1) quantify the effectiveness of a site-specific sprayer, 2) evaluate influence of diffeent design parameters on efficiency of the site-specific sprayer. In each iteration of this model, infected areas (weed patches) in the field were randomly generated and the amount of required herbicides for spraying these areas were calculated. The effectiveness of the sprayer was estimated for different stain sizes, nozzle types (conic and flat), nozzle sizes and stain detection levels of the identification system. Simulation results indicated that the flat nozzle is much more effective as compared to the conic nozzle and its relative efficiency is greater for small nozzle sizes. By using a site-specific sprayer, the average ratio between the spraying areas and the stain areas is about 1.1 to 1.8 which can save up to 92% of herbicides, especially when the proportion of the stain areas is small.

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