Performance potential is an important concept in the sensitivity analysis of Markov chains. The estimation of performance potential provides the basis for the simulation-based optimization and sensitivity analysis of Markov chains. In this study, we present novel estimation approaches for the average reward (or cost) performance potential by combining perturbation realization factors and coupling techniques for Markov chains with finite state space. These approaches can effectively implement estimation with geometric variance reduction for average reward performance potential. Meanwhile, a number of coupling methods, including two optimal coupling methods, can be applied to further reduce estimation variance or simulation time. The numerical tests show that our approaches can significantly enhance the simulation efficiency.
Automatica – Elsevier
Published: Jul 1, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera