Results 261 to 270 of about 58,720 (307)

Dynamical and Stochastic Analysis of a Piezoelectric Neuron Model for Intelligent Sensing Applications. [PDF]

Sensors (Basel)
Abdelkader A, Ehsan H, Jhangeer A.
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Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks. [PDF]

Proc Natl Acad Sci U S A
Gao ML, Williams JP, Kutz JN.
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Decoding THz-Driven Dynamic Fingerprints of Ferroelectric Nanotwin Networks. [PDF]

Adv Mater
Li X, Ross A, Stoica VA, Das S, Hazra S, Wang HH, Padma H, Hoffmann MC, Kramer P, Song S, Nelson S, Sato T, Zhu D, Ramesh R, Martin LW, Cao Y, Freeland JW, Lindenberg AM, Wen H, Chen LQ, Gopalan V.   +20 more
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Modeling Dynamics in Crowdfunding

Marketing Science, 2020
This paper investigates the underlying mechanisms of crowdfunding behavior (forward-looking delaying investment behavior and social interactions), which lead to the crowdfunding dynamics.
Chul Kim, P. K. Kannan 0001, Michael Trusov, Andrea Ordanini   +3 more
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Dynamic topic models

Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics.
David M. Blei, John D. Lafferty
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Dynamic Model of Excavator

Journal of Aerospace Engineering, 1993
Automation of excavation work calls for a robotic system able to perform the planned digging work that is responsive to interaction forces experienced during excavation. The development of automated excavation control methods requires a dynamic model to describe the evolution of the excavator motion with time.
Vähä, Pentti, Skibniewski, Miroslaw
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Dynamical model for thyroid

Communications in Nonlinear Science and Numerical Simulation, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gholam Reza Rokni Lamooki, Amir H. Shirazi, Ali R. Mani   +2 more
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A Dynamic Network Loading Model for Traffic Dynamics Modeling

IEEE Transactions on Intelligent Transportation Systems, 2007
The need for a better representation of traffic dynamics and the reproduction of traffic flow motion on the network have been the main reasons to seek solutions for dynamic network loading (DNL) models. In this paper, a neural network (NN) approximator that supports the DNL model is utilized to model link flow dynamics on a sample network.
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Models of dynamical systems

2003
Dynamical systems can be modelled from different viewpoints. This chapter summarises the main notions. Each of the succeeding chapters uses one of these models for fault diagnosis and fault-tolerant control.
Mogens Blanke, Michel Kinnaert, Jan Lunze, Marcel Staroswiecki   +3 more
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