In this paper we generalize a bounded Markov process, described by Stoyanov and Pacheco-González for a class of transition probability functions. A recursive integral equation for the probability density of these bounded Markov processes is derived and the stationary probability density is obtained by solving an equivalent differential equation. Examples of stationary densities for different transition probability functions are given and an application for designing a robotic coverage algorithm with specific emphasis on particular regions is discussed.
In this paper we generalize a bounded Markov process, described by Stoyanov and Pacheco-González for a class of transition probability functions. A recursive integral equation for the probability density of these bounded Markov processes is derived and the stationary probability density is obtained by solving an equivalent differential equation. Examples of stationary densities for different transition probability functions are given and an application for designing a robotic coverage algorithm with specific emphasis on particular regions is discussed.
The equivalence of the methods of stochastic averaging and stochastic normal forms is demonstrated for systems under the effect of linear multiplicative and additive noise. It is shown that both methods lead to reduced systems with the same Markovian approximation. The key result is that the second-order stochastic terms have to be retained in the normal form computation. Examples showing applications to systems undergoing divergence and flutter instability are provided. Furthermore, it is shown that unlike stochastic averaging, stochastic normal forms can be used in the analysis of nilpotent systems to eliminate the stable modes. Finally, some results pertaining to stochastic Lorenz equations are presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.