کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
695090 | 1460645 | 2016 | 12 صفحه PDF | دانلود رایگان |
This work is concerned with optimal harvesting problems in random environments. In contrast to the existing literature, the Markov chain is hidden and can only be observed in a Gaussian white noise in our work. We first use the Wonham filter to estimate the Markov chain from the observable evolution of the given process so as to convert the original problem to a completely observable one. Then we treat the resulting optimal control problem. Because the problem is virtually impossible to solve in closed form, our main effort is devoted to developing numerical approximation algorithms. To approximate the value function and optimal strategies, Markov chain approximation methods are used to construct a discrete-time controlled Markov chain. Convergence of the algorithm is proved by weak convergence method and suitable scaling. A numerical example is provided to demonstrate the results.
Journal: Automatica - Volume 70, August 2016, Pages 74–85