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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

Last updated: 31 July 2025

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Journal articles on the topic "Stochastic algorithms parameters identification"

1

Zhang, Ce, Xiangxiang Meng, and Yan Ji. "Parameter Estimation of Fractional Wiener Systems with the Application of Photovoltaic Cell Models." Mathematics 11, no. 13 (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 forg

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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

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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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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 channe

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Ma, Ping, and Lei Wang. "Partially Coupled Stochastic Gradient Estimation for Multivariate Equation-Error Systems." Mathematics 10, no. 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 t

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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 (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 metahe

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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 recur

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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 (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

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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 (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

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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

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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 Oce

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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,

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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. Conventio

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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

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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 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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R, Kumar. A novel multistage estimation of the signal parameters of a possibly data-modulated sinusoid under very high dynamics. 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. 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 determinist

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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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System identification with quantized observations. Birkhäuser, 2010.

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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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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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Vermenych, Yaroslava. Society of the ukrainian-russian borderland in the coordinates of modern civilizational challenges: existential and security parameters. Analytical note. Institute of History of Ukraine, National Academy of Sciences of Ukraine, 2023. https://doi.org/10.15407/book1-0017654.

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The crisis parameters of the development of the ukrainian-russian border society are analyzed in the coordinates of the civilizational borderland, marked by the "intersection" of controversial socio-cultural markers, identification matrices, local values, meanings and symbols. Using the conceptual principles of transitology, the strategies of social transformations and cultural practices in the border space, the mechanisms of identification confrontations and mental confrontations are considered. The impact of borderland on spatial and socio-cultural dynamics and the increase in the risks of t

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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 stoc

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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. 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. 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. 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. 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. 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). 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. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83334-2_34.

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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. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-45257-7_12.

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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. 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. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36564-8_6.

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Conference papers on the topic "Stochastic algorithms parameters identification"

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Jia, Bowen, Wei Hong, Menghao Zhao, Yun Ma, Yanzheng Bai, and Zebing Zhou. "On-orbit identification algorithms for dynamics parameters of gravity satellite." In Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), edited by Xiangjie Kong and Xingjian Wang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049248.

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Chen, Bo-Jhih, Jen-Han Lin, Yu-Shan Cheng, Jia-Hong Fang, and Yi-Hua Liu. "Parameter Identification of the Parameters of Lithium-Ion Battery Model Based on Metaheuristic Algorithms." In 2024 16th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2024. http://dx.doi.org/10.1109/iiai-aai63651.2024.00154.

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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 numb

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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 p

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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

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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 m

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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 ta

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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. Defense Technical Information Center, 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.], 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

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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, 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 detecti

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Miles, Gaines E., Yael Edan, F. Tom Turpin, et al. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, 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 st

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