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Titlebook: Stochastic Differential Systems, Stochastic Control Theory and Applications; Proceedings of a Wor Wendell Fleming,Pierre-Louis Lions Confer

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发表于 2025-3-21 19:20:02 | 显示全部楼层 |阅读模式
书目名称Stochastic Differential Systems, Stochastic Control Theory and Applications
副标题Proceedings of a Wor
编辑Wendell Fleming,Pierre-Louis Lions
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丛书名称The IMA Volumes in Mathematics and its Applications
图书封面Titlebook: Stochastic Differential Systems, Stochastic Control Theory and Applications; Proceedings of a Wor Wendell Fleming,Pierre-Louis Lions Confer
描述This IMA Volume in Mathematics and its Applications STOCHASTIC DIFFERENTIAL SYSTEMS, STOCHASTIC CONTROL THEORY AND APPLICATIONS is the proceedings of a workshop which was an integral part of the 1986-87 IMA program on STOCHASTIC DIFFERENTIAL EQUATIONS AND THEIR APPLICATIONS. We are grateful to the Scientific Committee: Daniel Stroock (Chairman) WendeIl Flerning Theodore Harris Pierre-Louis Lions Steven Orey George Papanicolaou for planning and implementing an exciting and stimulating year-long program. We es­ pecially thank WendeIl Fleming and Pierre-Louis Lions for organizing an interesting and productive workshop in an area in which mathematics is beginning to make significant contributions to real-world problems. George R. Seil Hans Weinberger PREFACE This volume is the Proceedings of a Workshop on Stochastic Differential Systems, Stochastic Control Theory, and Applications held at IMA June 9-19,1986. The Workshop Program Commit­ tee consisted of W.H. Fleming and P.-L. Lions (co-chairmen), J. Baras, B. Hajek, J.M. Harrison, and H. Sussmann. The Workshop emphasized topics in the following four areas. (1) Mathematical theory of stochastic differential systems, stochastic control a
出版日期Conference proceedings 1988
关键词Markov chain; Markov decision process; Markov process; Parameter; Vari; algorithms; diffusion process; filt
版次1
doihttps://doi.org/10.1007/978-1-4613-8762-6
isbn_softcover978-1-4613-8764-0
isbn_ebook978-1-4613-8762-6Series ISSN 0940-6573 Series E-ISSN 2198-3224
issn_series 0940-6573
copyrightSpringer-Verlag New York Inc. 1988
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An Approximate Minimum Principle for a Partially Observed Markov Chain,ntrol is obtained using a variational inequality of Ekeland [3], and the co-state variables are identified. The results of this paper are an outcome of the author’s visit to the University of Maryland in the spring of 1986 and he wishes to express his gratitude to Professor Baras and the Systems Research Center.
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,Optimality of “full bang to reduce predicted miss” for some partially observed stochastic control pon to date, if no further control is exerted. For partially observed problems with bounded control, similar to some proposed by Åström, we use PDE methods to show that predicted miss defines the switching surfaces for an optimal “bang-bang” control law whose gist, simply stated, is to reduce predict
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On Some Approximation Techniques in Non Linear Filtering,, w. are two independent Wiener processes, and ξ is a random variable independent of w., w.. The parameter ε is small and 0 < γ < ½. This assumption means that there is a high signal to noise ratio. Let
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Applications of Homogenization Theory to the Control of Flexible Structures,s of continua, e.g., trusses may be modeled by beam equations. Using a technique from the mathematics of asymptotic analysis called ., we show how such approximations may be derived in a systematic way which avoids errors made using “direct” averaging methods. We also develop a model for the combine
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Some Results on Kolmogoroff Equations for Infinite Dimensional Stochastic Systems,pace ., . is a smooth function from . to ., .. is a .-valued Brownian motion in a probability space (Ω, ε, .) and . is another Hilbert space. Finally .: .(.) ⊂ . → .(.; .) is a linear operator (generally unbounded) from . into .(.; .). . is a positive nuclear operator in . such that .(..) = ..
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An Approximate Minimum Principle for a Partially Observed Markov Chain,ormalized densities. This approach was introduced by Davis [1], [2]. An optimal principle for a partially observed Markov process was also obtained by Rishel in [9]. The observation process is a point process whose intensity is a function of the Markov chain. The set of admissible controls is shown
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