Titlebook: Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology; David Holcman Book 2017 Springer Inte

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es are proposed for a general class of nonlinear discretetime systems. Later, the development of nonlinear estimation techniques for a class of nonlinear discrete-time systems is introduced. Finally, neural networks control strategies are presented for controlling a class of nonlinear discrete-time

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Chuan Xue,Gregory Jamesonnearest points to the links on obstacles are often assumed to be known or given, we consider the case of obstacles with convex hull and formulate another time-varying QP problem to compute the critical points on the manipulator. Since this problem is not strictly convex, an existing recurrent neural

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Hanne Hoitzing,Iain G. Johnston,Nick S. Joneshard, real-time flows results in the same ormarginally better QoS as no flow control. However, the bandwidth usage of actively controlled flows is significantly lower than that of uncontrolled flows. The scalability of the proposed active control schemes is acceptable.

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eneeds to adopt the framework of stochastic reaction-diffusion models, while in the latter, one can describe the processes by adopting the framework of Markov jump processes and stochastic differential equation978-3-319-87358-9978-3-319-62627-7

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Coagulation-Fragmentation with a Finite Number of Particles: Models, Stochastic Analysis, and Application, the mean-field approximation, and jump processes used to compute first passage times to a finite size cluster. These models become even more relevant for extracting parameters from live cell imaging data.

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