Готові списки джерел за темами / Fixed Exponential Kernel

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Добірка наукової літератури з теми "Fixed Exponential Kernel"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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Статті в журналах з теми "Fixed Exponential Kernel"

Jacob, Ashwin, Diptapriyo Majumdar, and Venkatesh Raman. "Expansion Lemma—Variations and Applications to Polynomial-Time Preprocessing." Algorithms 16, no. 3 (2023): 144. http://dx.doi.org/10.3390/a16030144.

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In parameterized complexity, it is well-known that a parameterized problem is fixed-parameter tractable if and only if it has a kernel—an instance equivalent to the input instance, whose size is just a function of the parameter. The size of the kernel can be exponential or worse, resulting in a quest for fixed-parameter tractable problems with polynomial-sized kernels. The developments in machinery (showing lower bounds for the sizes of the kernels) have led researchers to question what are the asymptotically optimum sizes for the kernels of fixed-parameter tractable problems. In this article,

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Tsuchida, Russell, Tim Pearce, Chris Van der Heide, Fred Roosta, and Marcus Gallagher. "Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 9967–77. http://dx.doi.org/10.1609/aaai.v35i11.17197.

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Analysing and computing with Gaussian processes arising from infinitely wide neural networks has recently seen a resurgence in popularity. Despite this, many explicit covariance functions of networks with activation functions used in modern networks remain unknown. Furthermore, while the kernels of deep networks can be computed iteratively, theoretical understanding of deep kernels is lacking, particularly with respect to fixed-point dynamics. Firstly, we derive the covariance functions of multi-layer perceptrons (MLPs) with exponential linear units (ELU) and Gaussian error linear units (GELU)

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Nurpadilah, Winda, I. Made Sumertajaya, and Muhamad Nur Aidi. "Geographically Weighted Regression with Kernel Weighted Function on Poverty Cases in West Java Province." Indonesian Journal of Statistics and Its Applications 5, no. 1 (2021): 173–81. http://dx.doi.org/10.29244/ijsa.v5i1p173-181.

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Spatial regression analysis is a form of regression model that considers spatial effects. Geographically weighted regression (GWR) is the spatial regression methods that can be used to deal with the problem of spatial diversity. This method generates local model parameter estimates for each observation location. The application of spatial statistics can be done in all areas such as the problem of poverty. Poverty can be influenced by factors of proximity between regions, so that in determining the poverty factor, the proximity factor of the region cannot be ignored. West Java Province is a pro

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Rahmawati, Anis Dyah, Aji Hamim Wigena, and Muhammad Nur Aidi. "Influencing factors for the human development index in West Java using geographically and temporally weighted regression kernel functions." Jurnal Pendidikan Geografi: Kajian, Teori, dan Praktek dalam Bidang Pendidikan dan Ilmu Geografi 28, no. 2 (2023): 228. http://dx.doi.org/10.17977/um017v28i22023p228-241.

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Human Development Index (HDI) is a competitive index that serves as one of the crucial metrics for evaluating the effectiveness of enhancing the quality of human resources. HDI values from different areas can be compared. This study aims to spatially and temporally explore the HDI data from districts or cities in West Java and examine the factors that influence HDI in each of these districts or cities using the GTWR Great Circle Distance Fixed Kernels model. In this study, we used a combination of cross-sectional data from districts or cities in West Java and time series data with seven annual

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Azevedo, J. S., S. M. Afonso, and M. P. G. Silva. "Numerical Analysis of the Chebyshev Collocation Method for Functional Volterra Integral Equations." TEMA (São Carlos) 21, no. 3 (2020): 521. http://dx.doi.org/10.5540/tema.2020.021.03.521.

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The collocation method based on Chebyshev basis functions, coupled Picard iterative process, is proposed to solve a functional Volterra integral equation of the second kind. Using the Banach Fixed Point Theorem, we prove theorems on the existence and uniqueness solutions in the L2-norm. We also provide the convergence and stability analysis of the proposed method, which indicates that the numerical errors in the L2-norm decay exponentially, provided that the kernel function is sufficiently smooth. Numerical results are presented and they confirm the theoretical prediction of the exponential ra

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Azkiya, Azka Al, Yenni Angraini, and Rahma Anisa. "Penerapan Geographically Weighted Panel Regression dan Data Envelopment Analysis dalam Pemodelan Kemiskinan di Kalimantan Timur." Journal of Regional and Rural Development Planning 8, no. 1 (2024): 41–53. http://dx.doi.org/10.29244/jp2wd.2024.8.1.41-53.

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Indonesia currently still needs to focus on achieving sustainable development goals agreed by all countries in the world. Indonesia presently ranks 82nd out of 163 nations in terms of SDG accomplishment, indicating that there is still plenty of potential for improvement. One of the goals that hasn't been accomplished is ‘no poverty’. Regarding the poverty cases, among all province in Indonesia, East Kalimantan is important to be analyzed, because Penajam Paser Utara and Kutai Kartanegara in East Kalimantan are scheduled to become Indonesia's next capital, Nusantara. The goal of this research i

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Martha, Shantika, Yundari Yundari, Setyo Wira Rizki, and Ray Tamtama. "PENERAPAN METODE GEOGRAPHICALLY WEIGHTED PANEL REGRESSION (GWPR) PADA KASUS KEMISKINAN DI INDONESIA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 15, no. 2 (2021): 241–48. http://dx.doi.org/10.30598/barekengvol15iss2pp241-248.

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To analyze the factor affecting poverty during several periods by considering some geographical factors, we can use a geographically weighted panel regression (GWPR) method. GWPR is a combination of the geographically weighted regression (GWR) model and the panel regression model. The research conducts to identify the factors affecting the percentage of poor people in 34 provinces in Indonesia during 2015-2019. The results show that a suitable GWPR model is a fixed-effect model (FEM) with an exponential adaptive kernel function. Referring to the model, the province is divided into four groups

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Dokuyucu, Mustafa, and Hemen Dutta. "Analysis of a fractional tumor-immune interaction model with exponential kernel." Filomat 35, no. 6 (2021): 2023–42. http://dx.doi.org/10.2298/fil2106023d.

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In this paper, a tumor-immune interaction model has been analyzed via Caputo-Fabrizio fractional derivative operator with exponential kernel. Existence of solution of the model has been established with a fixed-point method and then it demonstrated the uniqueness of solution also. The stability of the model has been analyzed with the help of Hyers-Ulam stability approach and then numerical solution by using the Adam-Basford method. The results are further examined in detail with simulations for different fractional derivative values.

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Munteanu, Ionuţ. "Feedback exponential stabilization of the semilinear heat equation with nonlocal initial conditions." Nonlinear Analysis: Modelling and Control 26, no. 6 (2021): 1106–22. http://dx.doi.org/10.15388/namc.2021.26.24809.

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The present paper is devoted to the problem of stabilization of the one-dimensional semilinear heat equation with nonlocal initial conditions. The control is with boundary actuation. It is linear, of finite-dimensional structure, given in an explicit form. It allows to write the corresponding solution of the closed-loop equation in a mild formulation via a kernel, then to apply a fixed point argument in a convenient space.

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Swetha, Stanislaus Jasmin, Velusamy Kavitha, Mani Mallika Arjunan, and Dumitru Baleanu. "ON THE INITIAL VALUE PROBLEMS FOR NEUTRAL INTEGRO-DIFFERENTIAL SYSTEM WITHIN EXPONENTIAL KERNEL." Annals of the Academy of Romanian Scientists Series on Mathematics and Its Application 16, no. 1 (2024): 5–23. http://dx.doi.org/10.56082/annalsarscimath.2024.1.5.

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This paper deals with the existence results for neutral integro-differential system (NIDS) through Caputo-Fabrizio (CF) operator. The solution to our addressing system, which can be considered innovative, is explained and proven in preliminary section. Certain novel existence findings are shown using fixed point approaches. Finally, two numerical examples are provided in the work to demonstrate the application of our theoretical findings.

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Більше джерел

Дисертації з теми "Fixed Exponential Kernel"

Hakala, Tim. "Settling-Time Improvements in Positioning Machines Subject to Nonlinear Friction Using Adaptive Impulse Control." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/1061.

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A new method of adaptive impulse control is developed to precisely and quickly control the position of machine components subject to friction. Friction dominates the forces affecting fine positioning dynamics. Friction can depend on payload, velocity, step size, path, initial position, temperature, and other variables. Control problems such as steady-state error and limit cycles often arise when applying conventional control techniques to the position control problem. Studies in the last few decades have shown that impulsive control can produce repeatable displacements as small as ten nanomete

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Тези доповідей конференцій з теми "Fixed Exponential Kernel"

Tran, Anh, Kathryn Maupin, Ankush Kumar Mishra, Zhen Hu, and Chao Hu. "Additive Gaussian Process Regression for Metamodeling in High-Dimensional Problems." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-116555.

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Abstract Gaussian process (GP) regression is an important scientific machine learning (ML) tool that naturally embeds uncertainty quantification (UQ) in a Bayesian context. Thanks to its rigorous mathematical foundation and not-so-many hyperparameters, GP has been one of the most commonly used ML tools for a wide range of engineering applications, including UQ, sensitivity analysis, and design optimization, among others. As same for many similar functional approximation methods, GP also suffers from the curse of dimensionality, where its accuracy degrades exponentially as the number of input d

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