Bounded Noises in Physics, Biology, and Engineering
Since the parameters in dynamical systems of biological interest are inherently positive and bounded, bounded noises are a natural way to model the realistic stochastic fluctuations of a biological system that are caused by its interaction with the external world. Bounded Noises in Physics, Biology,...
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| Language: | English |
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New York, NY :
Springer New York : Imprint: Birkhäuser,
2013.
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| Edition: | 1st ed. 2013. |
| Series: | Modeling and Simulation in Science, Engineering and Technology,
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| Online Access: | https://doi.org/10.1007/978-1-4614-7385-5 |
Table of Contents:
- Introduction
- Part I : Modeling of Bounded Noises and Their Applications in Physics
- On Bounded Stochastic Processes
- Dynamics of Systems With Randomly Disordered Periodic Excitations
- Noise-Induced Phenomena: Effects of Noises Based on Tsallis Statistics
- Dynamical Systems Driven by Dichotomous Noise
- Stochastic Oscillator : Brownian Motion With Adhesion
- Numerical Study of Energetic Stability For Harmonic Oscillator With Fluctuating Damping Parameter
- A Moment-Based Approach to Bounded Non-Gaussian Colored Noise
- Spatiotemporal Bounded Noises, and Their Application to the Ginzburg-Landau Equation
- Part II: Bounded Noises in the Framework of Discrete and Continuous Random Dynamical Systems
- Bifurcations of Random Differential Equations With Bounded Noise
- Effects of Bounded Random Perturbations on Discrete Dynamical Systems
- Part III: Bounded Stochastic Fluctuations in Biology
- Bounded Stochastic Perturbations May Induce Non-Genetic Resistance to Anti-Tumor Chemotherapy
- Interplay Between Cross Correlation and Delays in the Sine-Wienernoise-Induced Transitions
- Bounded Extrinsic Noises Affecting Biochemical Networks With Low Molecule Numbers
- Part IV: Bounded Noises: Applications in Engineering
- Almost Sure Stability of Fractional Viscoelastic Systems Driven By Bounded Noises
- Model Selection for Random Functions With Bounded Range. Applications in Science and Engineering
- From Model-Based to Data-Driven Filter Design.