Relevant bibliographies by topics / Spatiotemporal identification

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  1. Journal articles

Academic literature on the topic 'Spatiotemporal identification'

Author: Grafiati
Published: 14 April 2023

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Journal articles on the topic "Spatiotemporal identification"

1

Lakumarapu, Srikanth, and Rashmi Agarwal. "Cramming Identification through Spatiotemporal Data." International Journal of Computer Sciences and Engineering 6, no. 6 (2018): 693–701. http://dx.doi.org/10.26438/ijcse/v6i6.693701.

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2

Voss, H., M. Bünner, and M. Abel. "Identification of continuous, spatiotemporal systems." Physical Review E 57, no. 3 (1998): 2820–23. http://dx.doi.org/10.1103/physreve.57.2820.

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3

PAN, Y., and S. A. BILLINGS. "THE IDENTIFICATION OF COMPLEX SPATIOTEMPORAL PATTERNS USING COUPLED MAP LATTICE MODELS." International Journal of Bifurcation and Chaos 18, no. 04 (2008): 997–1013. http://dx.doi.org/10.1142/s021812740802080x.

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Many complex and interesting spatiotemporal patterns have been observed in a wide range of scientific areas. In this paper, two kinds of spatiotemporal patterns including spot replication and Turing systems are investigated and new identification methods are proposed to obtain Coupled Map Lattice (CML) models for this class of systems. Initially, a new correlation analysis method is introduced to determine an appropriate temporal and spatial data sampling procedure for the identification of spatiotemporal systems. A new combined Orthogonal Forward Regression and Bayesian Learning algorithm with Laplace priors is introduced to identify sparse and robust CML models for complex spatiotemporal patterns. The final identified CML models are validated using correlation-based model validation tests for spatiotemporal systems. Numerical results illustrate the identification procedure and demonstrate the validity of the identified models.
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4

Pan, J. B., S. C. Hu, H. Wang, Q. Zou, and Z. L. Ji. "PaGeFinder: quantitative identification of spatiotemporal pattern genes." Bioinformatics 28, no. 11 (2012): 1544–45. http://dx.doi.org/10.1093/bioinformatics/bts169.

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5

Conkling, Tara J., James A. Martin, Jerrold L. Belant, and Travis L. DeVault. "Spatiotemporal Dynamics in Identification of Aircraft–Bird Strikes." Transportation Research Record: Journal of the Transportation Research Board 2471, no. 1 (2015): 19–25. http://dx.doi.org/10.3141/2471-03.

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6

Pan, Y., and S. A. Billings. "Neighborhood Detection for the Identification of Spatiotemporal Systems." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 3 (2008): 846–54. http://dx.doi.org/10.1109/tsmcb.2008.918571.

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7

Ning, Hanwen, Xingjian Jing, and Li Cheng. "Identification of non-linear stochastic spatiotemporal dynamical systems." IET Control Theory & Applications 7, no. 17 (2013): 2069–83. http://dx.doi.org/10.1049/iet-cta.2013.0150.

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8

Krakover, Shaul. "Identification of Spatiotemporal Paths of Spread and Backwash." Geographical Analysis 15, no. 4 (2010): 318–29. http://dx.doi.org/10.1111/j.1538-4632.1983.tb00790.x.

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9

Ellison, Adrian B., Richard B. Ellison, Asif Ahmed, Dean Rance, and Stephen P. Greaves. "Spatiotemporal Identification of Trip Stops from Smartphone Data." Applied Spatial Analysis and Policy 12, no. 1 (2016): 27–43. http://dx.doi.org/10.1007/s12061-016-9188-0.

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10

Dong, Xunde, and Cong Wang. "Identification of the Gray–Scott Model via Deterministic Learning." International Journal of Bifurcation and Chaos 31, no. 04 (2021): 2150051. http://dx.doi.org/10.1142/s0218127421500516.

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Abstract:
Gray–Scott model is one of the most well-known reaction–diffusion models which has a wealth of spatiotemporal chaos behavior. It is commonly used to study spatiotemporal chaos. In the paper, a novel method is proposed for the identification of the Gray–Scott model via deterministic learning and interpolation. The method mainly consists of two phases: the local identification phase and the global identification phase. Local identification is achieved using the finite difference method and deterministic learning. Based on the local identification results, the interpolation method is employed to obtain global identification. Numerical experiments show the feasibility and effectiveness of the proposed method.
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