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Relevant bibliographies by topics / Generalized modulating functions estimation method

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Academic literature on the topic 'Generalized modulating functions estimation method'

Author: Grafiati

Published: 12 April 2025

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Journal articles on the topic "Generalized modulating functions estimation method"

1

Tian, Yang, Yan-Qiao Wei, Da-Yan Liu, and Driss Boutat. "Fast and robust estimation for positions and velocities from noisy accelerations using generalized modulating functions method." Mechanical Systems and Signal Processing 133 (November 2019): 106270. http://dx.doi.org/10.1016/j.ymssp.2019.106270.

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2

Liu, Da-Yan, and Taous-Meriem Laleg-Kirati. "Robust fractional order differentiators using generalized modulating functions method." Signal Processing 107 (February 2015): 395–406. http://dx.doi.org/10.1016/j.sigpro.2014.05.016.

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3

Pumaricra Rojas, David, Matti Noack, Johann Reger, and Gustavo Pérez-Zúñiga. "State Estimation for Coupled Reaction-Diffusion PDE Systems Using Modulating Functions." Sensors 22, no. 13 (2022): 5008. http://dx.doi.org/10.3390/s22135008.

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Many systems with distributed dynamics are described by partial differential equations (PDEs). Coupled reaction-diffusion equations are a particular type of these systems. The measurement of the state over the entire spatial domain is usually required for their control. However, it is often impossible to obtain full state information with physical sensors only. For this problem, observers are developed to estimate the state based on boundary measurements. The method presented applies the so-called modulating function method, relying on an orthonormal function basis representation. Auxiliary sy

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4

Fedele, Giuseppe, and Loredana Coluccio. "A recursive scheme for frequency estimation using the modulating functions method." Applied Mathematics and Computation 216, no. 5 (2010): 1393–400. http://dx.doi.org/10.1016/j.amc.2010.02.039.

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5

He, Shanghong. "PARAMETER ESTIMATION OF LINEAR CONTINUOUS-TIME DYNAMIC SYSTEM USING MODULATING FUNCTIONS METHOD." Chinese Journal of Mechanical Engineering 39, no. 12 (2003): 129. http://dx.doi.org/10.3901/jme.2003.12.129.

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6

Lu, Jing-Yi, Dong Ye, and Wen-Ping Ma. "Time delay estimation based on variational mode decomposition." Advances in Mechanical Engineering 9, no. 1 (2017): 168781401668858. http://dx.doi.org/10.1177/1687814016688587.

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In order to improve the time delay estimation of colored noise signals, this article proposes generalized cross-correlation time delay estimation based on variational mode decomposition. First of all, we put forward the signal energy detection criterion to extract the effective signal from the signal, which can reduce the amount of calculation and improve the real-time performance. Second, the effective signal is decomposed into a number of intrinsic mode functions using variational mode decomposition. The correlation coefficients of each intrinsic mode function and the original signal are cal

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7

Gong, Qin, and Bin Yin. "Statistical inference of entropy functions of generalized inverse exponential model under progressive type-II censoring test." PLOS ONE 19, no. 9 (2024): e0311129. http://dx.doi.org/10.1371/journal.pone.0311129.

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This article explores the estimation of Shannon entropy and Rényi entropy based on the generalized inverse exponential distribution under the condition of stepwise Type II truncated samples. Firstly, we analyze the maximum likelihood estimation and interval estimation of Shannon entropy and Rényi entropy for the generalized inverse exponential distribution. In this process, we use the bootstrap method to construct confidence intervals for Shannon entropy and Rényi entropy. Next, we select the gamma distribution as the prior distribution and apply the Lindley approximation algorithm to calculat

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Rose, Charles E., Michael L. Clutter, Barry D. Shiver, Daniel B. Hall, and Bruce Borders. "A Generalized Methodology for Developing Whole-Stand Survival Models." Forest Science 50, no. 5 (2004): 686–95. http://dx.doi.org/10.1093/forestscience/50.5.686.

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Abstract A large source of variability in yield predictions is due to estimation of future surviving trees per unit area. Previous whole-stand survival modeling efforts have concentrated on modeling the empirical survival curve. Modeling hazard functions, an approach to survival analysis commonly used in fields such as medicine and sociology, can be applicable to plantation survival estimation. We offer a generalized method for deriving whole-stand survival models that are capable of modeling complex underlying hazard functions. We use our knowledge of the empirical hazard function to limit ou

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Wang, Liming, Songjun Han, and Fuqiang Tian. "At which timescale does the complementary principle perform best in evaporation estimation?" Hydrology and Earth System Sciences 25, no. 1 (2021): 375–86. http://dx.doi.org/10.5194/hess-25-375-2021.

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Abstract. The complementary principle has been widely used to estimate evaporation under different conditions. However, it remains unclear at which timescale the complementary principle performs best. In this study, evaporation estimations were conducted at 88 eddy covariance (EC) monitoring sites at multiple timescales (daily, weekly, monthly, and yearly) by using sigmoid and polynomial generalized complementary functions. The results indicate that the generalized complementary functions exhibit the highest skill in estimating evaporation at the monthly scale. The uncertainty analysis shows t

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10

Linton, Oliver B. "EFFICIENT ESTIMATION OF GENERALIZED ADDITIVE NONPARAMETRIC REGRESSION MODELS." Econometric Theory 16, no. 4 (2000): 502–23. http://dx.doi.org/10.1017/s0266466600164023.

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We define new procedures for estimating generalized additive nonparametric regression models that are more efficient than the Linton and Härdle (1996, Biometrika 83, 529–540) integration-based method and achieve certain oracle bounds. We consider criterion functions based on the Linear exponential family, which includes many important special cases. We also consider the extension to multiple parameter models like the gamma distribution and to models for conditional heteroskedasticity.

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