Journal Article10.1016/0020-0255(78)90027-0
General control systems
18
TL;DR: A model for interaction between random environments and random automata is proposed and the theory of (generalized) random systems with complete connections with learning models or dynamic models is applied in order to describe its behavior.
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About: This article is published in Information Sciences. The article was published on 01 Jan 1978. The article focuses on the topics: Random function.
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Citations
A Smooth Converse Lyapunov Theorem for Robust Stability
TL;DR: In this article, a converse Lyapunov function theorem motivated by robust control analysis and design is presented, which is based upon, but generalizes, various aspects of well-known classical theorems.
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Processes with long memory: Regenerative construction and perfect simulation
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Classical converse theorems in Lyapunov's second method
TL;DR: In this paper, the authors survey some of the available answers to the question "does there exist an appropriate Lyapunov function?" and present a survey of the existing answers.
102
On the dynamics of nonautonomous periodic general dynamical systems and differential inclusions
Desheng Li,Peter E. Kloeden +1 more
TL;DR: In this paper, the authors established the existence and stability results for periodic non-autonomous uniform forward attractors of periodic general dynamical systems (set-valued dynamical system) and investigated the dynamical behavior of nonautonomous periodic differential inclusion with respect to both internal and external perturbations.
40
Topological bifurcations of minimal invariant sets for set-valued dynamical systems
Jeroen S. W. Lamb,Martin Rasmussen,Christian S. Rodrigues +2 more
- 02 Apr 2015
TL;DR: In this article, the dependence of set-valued dynamical systems on parameters was discussed and it was shown that topological bifurcations of minimal invariant sets are discontinuous with respect to the Hausdorff metric, taking the form of lower semi-continuous explosions and instantaneous appearances.
References
•Book
Stochastic processes
J. L. Doob,Joseph L. Doob +1 more
- 01 Jan 1953
TL;DR: Stochastic processes are probabilistic models of data streams such as speech, audio and video signals, stock market prices, and measurements of physical phenomena by digital sensors such as medical instruments, GPS receivers, or seismographs that are essential for understanding phenomena and processing information.
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