Relevant bibliographies by topics / Parameter optimization

Academic literature on the topic 'Parameter optimization'

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Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "Parameter optimization"

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Rajeanderan Revichandran, Jaffar Syed Mohamed Ali, Moumen Idres, and A. K. M. Mohiuddin. "A Review of HVAC System Optimization and Its Effects on Saving Total Energy Utilization of a Building." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 93, no. 1 (March 25, 2022): 64–82. http://dx.doi.org/10.37934/arfmts.93.1.6482.

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The paper illustrates the review on the optimizations studies of HVAC systems based on three main methods – HVAC operational variables optimization, optimization of control parameters in HVAC system and parameter optimization in building models. For the HVAC system’s operational variables, the optimization process is based on the common and prescient energy utilization models. Thus, by comparing both, the non-common HVAC system models can get better output of energy reduction. Based on most of the studies, the occupancies thermal comfort requirements, are represented by the indoor air quality (IAQ) or the predicted mean vote (PMV) indexes. Comparing both requirements, the PMV index had a better overall energy reduction output of 47% and estimated annual energy reduction of 2,769 kg/year. Meanwhile, in optimization of HVAC’s control parameters, its overall aim is to achieve a better response output of the HVAC system in order to prevent energy wastage. Among this different optimization’s controller, the fuzzy logic tuning optimization has a better overall energy reduction. On the other hand, the parameter optimization in building model approach is performed before the construction of the structure itself, where multiple construction parameters are considerations in the design. In overall, when different tools for building parameter and model optimization are compared, the EXRETopt by using PMV comfort index approximately reduces 62% of the energy utilization.
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Zhong, Mei Peng. "Parameter Optimization of Compressor Based on an Ant Colony Optimization." Applied Mechanics and Materials 201-202 (October 2012): 916–19. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.916.

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A mathematical model of operation on air compressors is set up in order to improve the efficiency of air compressors. Parameter of Compressor is optimized by an Ant Colony Optimization (ACO) Particle approach. Volume and its weight of the new compressor are little, and its efficiency is high. An Ant Colony Optimization embed BLDCM module which optimizating the air compressor was put forward. Optimizated target of an Ant Colony Optimization is the efficiency of BLDCM. Optimizated variables are the diameter of low pressure cylinder, the diameter of high pressure cylinder, the journey of low pressure piston, the journey of high pressure piston and the rotate speed of BLDCM. Simulated result shows that the efficiency of BLDCM is more than that before optimizating. The test is done. The result shows that the specifc Power of air compressor is much less than before optimizating on 2.5Mpa. The result also shows that an Ant Colony Optimization which optimizating the air compressor is availability and practicality.
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Prinz, Astrid. "Neuronal parameter optimization." Scholarpedia 2, no. 1 (2007): 1903. http://dx.doi.org/10.4249/scholarpedia.1903.

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Ng, Chuan Huat, and Mohd Khairulamzari Hamjah. "Welding Parameter Optimization of Surface Quality by Taguchi Method." Applied Mechanics and Materials 660 (October 2014): 109–13. http://dx.doi.org/10.4028/www.scientific.net/amm.660.109.

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An experimental study of GTAW was conducted to determine the optimization of weld parameters on the droplet formation in the surface quality of weld pools. These optimization investigations consisted of welding current, welding speed and feed rate. The strength and surface quality of weld pool were measured for each specimen after the welding parameter optimizations and the effect of these parameters on droplet formation were researched. To consider these quality characteristics together in the selection of welding parameters, the Orthogonal Array of Taguchi method is adopted to analyze the effect of each welding parameter on the weld pool quality, and then to determine the welding parameters with the optimal weld pool quality.
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Niu, Xiang Jie. "The Optimization for PID Controller Parameters Based on Genetic Algorithm." Applied Mechanics and Materials 513-517 (February 2014): 4102–5. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4102.

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as an important research field of automatic control problems, PID parameter optimization's control effect depends on the proportional, integral and derivative values. Using trial and error testing to manually realize optimization PID parameters, the traditional ways are often time-consuming and difficult to meet the requirements of real-time control. In order to solve the problems and improve system performance, the paper proposes a PID parameter optimization strategy based on genetic algorithm. The paper establishes the PID controller parameter model through genetic algorithm, uses the PID parameters as individuals in genetic algorithm during the control process, and takes the integral function of absolute error control time as the optimization object to dynamically adjust the three PID control parameters, thus realize online optimization for PID control parameters. Simulation results show that the introduction of genetic algorithms for PID control system improves the dynamic performance, enhance system stability and operation speed, and get better control effect.
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Adil, H., A. A. Koser, M. S. Qureshi, and A. Gupta. "Sleep quality assessment by parameter optimization." Journal of Physics: Conference Series 2070, no. 1 (November 1, 2021): 012013. http://dx.doi.org/10.1088/1742-6596/2070/1/012013.

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Abstract Sleep quality measurement is a complex process requires large number of parameters to monitor sleep and sleep cycles. The Gold Standard Polysomnography (PSG) parameters are considered as standard parameters for sleep quality measurement. In the PSG process, number of monitoring parameters are involved for that large number of sensors are used which makes this process complex, expensive and obtrusive. There is need to find optimize parameters which are directly involve in providing accurate information about sleep and reduce the process complexity. Our Parameter Optimization method is based on parameter reduction by finding key parameters and their inter dependent parameters. Sleep monitoring by these optimize parameter is different from both, clinical complex (PSG) used in hospitals and commercially available devices which work on dependent and dynamic parameter sensing. Optimized parameters obtained from PSG parameters are Electrocardiogram (ECG), Electrooculogram (EOG), Electroencephalography (EEG) and Cerebral blood flow (CBF). These key parameters show close correlation with sleep and hence reduce complexity in sleep monitoring by providing simultaneous measurement of appropriate signals for sleep analysis.
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Kitamura, Makoto, Masahiro Umeda, Toshihiro Higuchi, Shouji Naruse, and Chuuzou Tanaka. "465. Optimization of a parameter in functional-MRI paramete." Japanese Journal of Radiological Technology 50, no. 8 (1994): 1380. http://dx.doi.org/10.6009/jjrt.kj00003326264.

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Vohra, Nilesh M. "Optimization of Cutting Parameter in Edm Using Taguchi Method." International Journal of Scientific Research 2, no. 1 (June 1, 2012): 90–95. http://dx.doi.org/10.15373/22778179/jan2013/32.

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Zala, Cedric A., and John M. Ozard. "Estimation of Geoacoustic Parameters from Narrowband Data Using a Search-Optimization Technique." Journal of Computational Acoustics 06, no. 01n02 (March 1998): 223–43. http://dx.doi.org/10.1142/s0218396x98000168.

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Geoacoustic parameters were estimated for vertical array data from the matched-field inversion benchmark data sets. Separate inversions were performed for narrowband data at 25 Hz, 50 Hz and 75 Hz, using a matching function consisting of the incoherent sum of the Bartlett outputs for the five vertical arrays at ranges of 1, 2, 3, 4 and 5 km. Parameter estimation was performed using a parabolic equation sound propagation algorithm to generate the replica fields, and a search-optimization technique to obtain estimates of the optimized parameter values. This technique involved an initial search stage in which the parameter space was sampled, and a second optimization stage in which each of a specified number of the best matches found in the search stage was used as the starting point for optimization. This approach provided multiple independent estimates of the geoacoustic parameters, and allowed assessment of the non-uniqueness of the problem and the sensitivity of the matching function to the individual parameters. A method was developed to combine the results for several frequencies to estimate the parameters. It used a weighted average with weights computed on the basis of the relative sensitivities at those frequencies; these sensitivities were estimated by the root-mean-square (RMS) gradient observed during the optimizations. Strong interdependencies among the parameters were found in the analysis, particularly between the sediment thickness and the sound speed at the bottom of the sediment. For the single-frequency matching function used here, it was observed that the inversion problems were ill-posed in that sets of parameter values from a wide region of the parameter space gave essentially perfect matches. The consistency of the parameter estimates was greatly improved by including a regularization term in the matching function. Regularized search-optimization provided an efficient method for estimating an effective geoacoustic model for acoustic field prediction.
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INOUE, Kazuya, and Mariko SUZUKI. "PARAMETER OPTIMIZATION USING SWARM INTELLIGENCE." Journal of Japan Society of Civil Engineers, Ser. A2 (Applied Mechanics (AM)) 74, no. 2 (2018): I_33—I_44. http://dx.doi.org/10.2208/jscejam.74.i_33.

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Dissertations / Theses on the topic "Parameter optimization"

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Marcinkevicius, Tadas. "DRAM BASED PARAMETER DATABASE OPTIMIZATION." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-100332.

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This thesis suggests an improved parameter database implementation for one of Ericsson products. The parameter database is used during the initialization of the system as well as during the later operation. The database size is constantly growing because the parameter database is intended to be used with different hardware configurations. When a new technology platform is released, multiple revisions with additional features and functionalities are later created, resulting in introduction of new parameters or changes to their values. Ericsson provides support for old and new products. The entire parameter database is currently stored in DRAM memory as a hash map. Therefore an optimal parameter database implementation should have low memory footprint. The search speed and initialization speed for the target system are also important to allow high system availability and low downtime, since a reboot is a common fix for many problems. As many optimizations have to consider memory size – speed tradeoff, it has been decided to give preference to reducing memory footprint. This research seeks to: Analyze data-structures suitable for parameter database implementation (Hash map, Sparsehash, Judy hash, Binary search tree, Treap, Skip List, AssocVector presorted using std::map, Burst trie). Propose the best data-structure in terms of used memory area and speed. If possible, further optimize it for database size in memory and access speed. Create a prototype implementation. Test the performance of the new implementation. The results indicate that a more compact database implementation can be achieved using alternative data structures such as Presorted AssocVector an Sparsehash, however some search speed and build speed is lost when using these data structures instead of the original Gnu Hash Map implementation.
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Koripalli, RadhaShilpa. "Parameter Tuning for Optimization Software." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2862.

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Mixed integer programming (MIP) problems are highly parameterized, and finding parameter settings that achieve high performance for specific types of MIP instances is challenging. This paper presents a method to find the information about how CPLEX solver parameter settings perform for the different classes of mixed integer linear programs by using designed experiments and statistical models. Fitting a model through design of experiments helps in finding the optimal region across all combinations of parameter settings. The study involves recognizing the best parameter settings that results in the best performance for a specific class of instances. Choosing good setting has a large effect in minimizing the solution time and optimality gap.
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Gupta, Deepak Prakash. "Energy sensitive machining parameter optimization model." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4406.

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Thesis (M.S.)--West Virginia University, 2005.
Title from document title page. Document formatted into pages; contains ix, 71 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 67-71).
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Eeles, Charles William Owen. "Parameter optimization of conceptual hydrological models." Thesis, Open University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261674.

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Thellner, Mikael. "Multi-parameter topology optimization in continuum mechanics /." Linköping : Dept. of Mechanical Engineering, Univ, 2005. http://www.bibl.liu.se/liupubl/disp/disp2005/tek934s.pdf.

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Herrmann, Todd Matthew. "A critical parameter optimization of launch vehicle costs." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3927.

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Thesis (M.S.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Dept. of Aerospace Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Mathavan, Neashan Graduate School of Biomedical Engineering Faculty of Engineering UNSW. "Parameter optimization in simplified models of cardiac myocytes." Awarded by:University of New South Wales. Graduate School of Biomedical Engineering, 2009. http://handle.unsw.edu.au/1959.4/44709.

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Atrial fibrillation (AF) is a complex, multifaceted arrhythmia. Pathogenesis of AF is associated with multiple aetiologies and the mechanisms by which it is sustained and perpetuated are similarly diverse. In particular, regional heterogeneity in the electrophysiological properties of normal and pathological tissue plays a critical role in the occurrence of AF. Understanding AF in the context of electrophysiological heterogeneity requires cell-specific ionic models of electrical activity which can then be incorporated into models on larger temporal and spatial scales. Biophysically-based models have typically dominated the study of cellular excitability providing detailed and precise descriptions in the form of complex mathematical formulations. However, such models have limited applicability in multidimensional simulations as the computational expense is too prohibitive. Simplified mathematical models of cardiac cell electrical activity are an alternative approach to these traditional biophysically-detailed models. Utilizing this approach enables the embodiment of cellular excitation characteristics at minimal computational cost such that simulations of arrhythmogensis in atrial tissue are conceivable. In this thesis, a simplified, generic mathematical model is proposed that characterizes and reproduces the action potential waveforms of individual cardiac myocytes. It incorporates three time-dependent ionic currents and an additional time-independent leakage current. The formulation of the three time-dependent ionic currents is based on 4-state Markov schemes with state transition rates expressed as nonlinear sigmoidal functions of the membrane potential. Parameters of the generic model were optimized to fit the action potential waveforms of the Beeler-Reuter model, and, experimental recordings from atrial and sinoatrial cells of rabbits. A nonlinear least-squares optimization routine was employed for the parameter fits. The model was successfully fitted to the Beeler-Reuter waveform (RMS error: 1.4999 mV) and action potentials recorded from atrial tissue (RMS error: 1.3398 mV) and cells of the peripheral (RMS error: 2.4821 mV) and central (RMS error: 2.3126 mV) sinoatrial node. Thus, the model presented here is a mathematical framework by which a wide variety of cell-specific AP morphologies can be reproduced. Such a model offers the potential for insights into possible mechanisms that contribute to heterogeneity and/or arrhythmia.
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Lee, Sang Heon. "Efficient design and optimization of robust parameter experiments." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/24328.

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Luna, Ortiz J. E. "Optimization of distributed parameter systems using transient simulators." Thesis, University of Manchester, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.503592.

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Mannai, Sébastien (Sébastien Karim). "Multi-parameter control for centrifugal compressor performance optimization." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90778.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 88-89).
The potential performance benefit of actuating inlet guide vane (IGV) angle, variable diffuser vane (VDV) angle and impeller speed to implement a multi-parameter control on a centrifugal compressor system is assessed. The assessment consists of first developing a one-dimensional meanline model for estimating performance of centrifugal compressor system followed by the formulation of a control framework incorporating the meanline model. Performance estimate of a representative centrifugal compressor system with adjustable IGV angle, VDV angle and impeller speed using the meanline model is in accord with available test data. The impeller performance estimate based on the meanline model is also in accord with computed results from Reynolds Average Navier-Stokes Equations. The simple control framework can be used to optimize on the fly the compressor operation to meet a specific mission requirement by selecting an appropriate combination of impeller speed, IGV and VDV angle settings. Desirable flow configurations with the required performance in response to specified operating needs have been obtained to serve as illustrations on the practical utility of the control framework. Results provide guidelines and attributes of compressor for achieving the required performance and operation at the system level through prioritizing the actuation of the adjustable parameters; for instance impeller speed would provide a high level of leverage to affect the compressor performance on an effective basis and that the IGV angle should be confined to a specified range. While the results have not been assessed in an experimental setting, they are used to design and plan an experimental program for evaluating the proposed simple multi-parameter control strategy. Flexibility have been incorporated into the formulation to allow the refinement and updating of the model for improved accuracy and fidelity.
Sebastien Mannai.
S.M.
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Books on the topic "Parameter optimization"

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Morelli, Eugene A. Practical input optimization for aircraft parameter estimation experiments. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1993.

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Romanovich, Statnikov Alexander, ed. The parameter space investigation method toolkit. Boston: Artech House, 2011.

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Optimal control of nonsmooth distributed parameter systems. Berlin: Springer-Verlag, 1990.

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Tiba, Dan. Optimal control of nonsmooth distributed parameter systems. Berlin: Springer-Verlag, 1990.

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Malanowski, Kazimierz, Zbigniew Nahorski, and Małgorzata Peszyńska, eds. Modelling and Optimization of Distributed Parameter Systems Applications to engineering. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-0-387-34922-0.

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Chen, Youyi. Extended quasi-likelihoods and optimal estimating functions. Toronto: University of Toronto, Dept. of Statistics, 1991.

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S, Korkhin Arnold, and SpringerLink (Online service), eds. Regression Analysis Under A Priori Parameter Restrictions. New York, NY: Springer Science+Business Media, LLC, 2012.

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Uciński, Dariusz. Optimal measurement methods for distributed parameter system identification. Boca Raton, Fla: CRC Press, 2005.

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Optimal measurement methods for distributed parameter system identification. Boca Raton, Fla: CRC Press, 2005.

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Schmidt, Phillip. A parameter optimization approach to controller partitioning for integrated flight/propulsion control application. [Washington, DC: National Aeronautics and Space Administration, 1992.

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Book chapters on the topic "Parameter optimization"

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Longuski, James M., José J. Guzmán, and John E. Prussing. "Parameter Optimization." In Optimal Control with Aerospace Applications, 1–17. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8945-0_1.

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Bhatnagar, S., H. Prasad, and L. Prashanth. "Discrete Parameter Optimization." In Stochastic Recursive Algorithms for Optimization, 151–66. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4285-0_9.

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Momber, Andreas W., and Radovan Kovacevic. "Process-Parameter Optimization." In Principles of Abrasive Water Jet Machining, 195–229. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1572-4_7.

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Ylli, Klevis, and Yiannos Manoli. "Geometrical Parameter Optimization." In Energy Harvesting for Wearable Sensor Systems, 43–53. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4448-8_3.

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Li, Li. "Parameter Estimations." In Springer Optimization and Its Applications, 53–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46356-7_3.

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Schittkowski, Klaus. "Parameter Estimation in Dynamic Systems." In Applied Optimization, 183–204. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4613-0301-5_13.

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Van Geit, Werner. "Hybrid Parameter Optimization Methods." In Encyclopedia of Computational Neuroscience, 1412–13. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_164.

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Van Geit, Werner. "Hybrid Parameter Optimization Methods." In Encyclopedia of Computational Neuroscience, 1–2. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_164-1.

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Zhao, Jun, Wei Wang, and Chunyang Sheng. "Parameter Estimation and Optimization." In Information Fusion and Data Science, 269–350. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94051-9_7.

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Kramer, Oliver. "Parameter Control." In A Brief Introduction to Continuous Evolutionary Optimization, 27–34. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03422-5_3.

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Conference papers on the topic "Parameter optimization"

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Tanyildizi, Erkan, and Fadime Demirtas. "Hiper Parametre Optimizasyonu Hyper Parameter Optimization." In 2019 1st International Informatics and Software Engineering Conference (UBMYK). IEEE, 2019. http://dx.doi.org/10.1109/ubmyk48245.2019.8965609.

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Zhang, Jiaolong, Jun Zhou, and Yaosheng Deng. "CubeSat Separation Parameter Optimization." In 2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS). IEEE, 2018. http://dx.doi.org/10.1109/icspcs.2018.8631745.

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Gupta, Rohit, and Ilya V. Kolmanovsky. "Governing Parameter Changes in Nonlinear Parameter-Dependent Optimization Problems." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3845.

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The paper treats a class of parameter-dependent optimization/root finding problems where the minimizer or a real root need to be determined as a function of parameter. Applications of parameter-dependent optimization include spacecraft debris avoidance, adaptive control of Hybrid Electric Vehicles, engine mapping and model predictive control. In these and other problems, the parameter changes can be controlled either directly or indirectly. In this paper, the error analysis of a dynamic predictor-corrector Newton’s type algorithm is presented. Based on this analysis, an approach to govern the changes in the parameter to enable the algorithm to track the minimizer within an acceptable error bound is described. Two simulation examples are presented. In the first example the objective is to minimize the distance between points on a curve and a given set and simultaneously move as fast as possible along the given curve. In the second example we illustrate the use of this technique for aircraft flight envelope estimation. Specifically, we estimate maximum speed of an aircraft as a function of its altitude and flight path angle.
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Maia, Renato Dourado, Leandro Nunes de Castro, and Walmir Matos Caminhas. "Real-parameter optimization with OptBees." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900549.

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Savenkov, Sergey N., Konstantin E. Yushtin, Borys M. Kolisnychenko, and Yuri A. Skoblya. "Dynamic Mueller-polarimeter parameter optimization." In BiOS Europe '97, edited by Francesco Baldini, Nathan I. Croitoru, Mark R. Dickinson, Martin Frenz, Mitsunobu Miyagi, Riccardo Pratesi, and Stefan Seeger. SPIE, 1998. http://dx.doi.org/10.1117/12.301119.

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Yadmellat, P., S. M. A. Salehizadeh, and M. B. Menhaj. "Fuzzy Parameter Particle Swarm Optimization." In 2008 First International Conference on Intelligent Networks and Intelligent Systems (ICINIS). IEEE, 2008. http://dx.doi.org/10.1109/icinis.2008.111.

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Yezzi, Anthony, and Navdeep Dahiya. "SHAPE ADAPTIVE ACCELERATED PARAMETER OPTIMIZATION." In 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). IEEE, 2018. http://dx.doi.org/10.1109/ssiai.2018.8470380.

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Ghosh, Jayeeta, Suresh Marru, Nikhil Singh, Kenno Vanomesslaeghe, Ye Fan, and Sudhakar Pamidighantam. "Molecular parameter optimization gateway (ParamChem)." In the 2011 TeraGrid Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2016741.2016779.

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Brunner, Christopher, Andrea Garavaglia, Mukesh Mittal, Mohit Narang, and Jose Bautista. "Inter-System Handover Parameter Optimization." In IEEE Vehicular Technology Conference. IEEE, 2006. http://dx.doi.org/10.1109/vtcf.2006.232.

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Xue, Fei, and Jiyi Liu. "Parameter Optimization Design of PMSM." In 2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA). IEEE, 2022. http://dx.doi.org/10.1109/icdsca56264.2022.9988400.

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Reports on the topic "Parameter optimization"

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D'Elia, Marta, and De lo Reyes, Juan Carlos, Miniguano, Andres. Bilevel parameter optimization for nonlocal image denoising models. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1592945.

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Edwards, D. A., and M. J. Syphers. Parameter selection for the SSC trade-offs and optimization. Office of Scientific and Technical Information (OSTI), October 1991. http://dx.doi.org/10.2172/67463.

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D'Elia, Marta, Juan Carlos De los Reyes, and Andres Trujillo. Bilevel parameter optimization for learning nonlocal image denoising models. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1617438.

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Vo, Duc Ta, and Michael Duncan Yoho. Optimization and Verification of FRAM Version 6.1 Parameter Sets. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1571575.

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Buchheit, Thomas E., Ian Zachary Wilcox, Andrew J. Sandoval, and Shahed Reza. Zener Diode Compact Model Parameter Extraction Using Xyce-Dakota Optimization. Office of Scientific and Technical Information (OSTI), December 2017. http://dx.doi.org/10.2172/1415117.

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Dimitrov, Evgeni, Hayden Schaeffer, David Wen, Sandra Rankovic, Kizza Nandyose, and Olivier Thonnard. The Construction of a Vague Fuzzy Measure Through L1 Parameter Optimization. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada567409.

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Fitzpatrick, Ben G. Statistical Techniques for Modeling, Estimation and Optimization in Distributed Parameter Systems. Fort Belvoir, VA: Defense Technical Information Center, February 1998. http://dx.doi.org/10.21236/ada383799.

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Ma, H. W., Y. Zhao, Z. Q. Wan, and J. Hu. PARAMETER OPTIMIZATION OF BEAM-COLUMN CONNECTIONS WITH EXPANDED FLANGE IN STEEL FRAMES. The Hong Kong Institute of Steel Construction, December 2018. http://dx.doi.org/10.18057/icass2018.p.067.

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Dennis, Jr, Williamson J. E., and Karen A. A New Parallel Optimization Algorithm for Parameter Identification in Ordinary Differential Equations. Fort Belvoir, VA: Defense Technical Information Center, September 1988. http://dx.doi.org/10.21236/ada455254.

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D'Elia, Marta, Christian Glusa, and Olena Burkovska. An optimization-based approach to parameter learning for fractional type nonlocal models. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1673822.

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