The Experts below are selected from a list of 18834 Experts worldwide ranked by ideXlab platform
Chi Huang - One of the best experts on this subject based on the ideXlab platform.
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Partial-Information-based synchronization analysis for complex dynamical networks
Journal of the Franklin Institute, 2015Co-Authors: Chi HuangAbstract:Abstract The model of complex dynamical networks with Partial Information transmission is introduced. Unlike existing complex network models, the Information transmission between the nodes is assumed to have an imperfect physical condition — only Partial Information can be transmitted. Furthermore, for varying connections, the partly transmitted Information can be completely distinct. This communication constraint makes the network harder to achieve synchronization. To reflect a more practical situation, we consider the complex networks with two typical kinds of inner coupling matrix: diagonal matrix and lower triangle matrix. By using an efficient decomposition method, some synchronization criteria are then derived for the complex networks with Partial Information transmission. Finally, a complex network with chaotic dynamics is constructed as an example to illustrate the effectiveness of the proposed results.
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Partial Information based distributed filtering in two targets tracking sensor networks
IEEE Transactions on Circuits and Systems, 2012Co-Authors: Chi HuangAbstract:In this paper, the Partial-Information-based (PIB) distributed filtering problem is addressed for two-targets tracking sensor networks. Different from existing distributed filters, the Information communication between the sensors are assumed to have an imperfect physical condition-only Partial Information can be transmitted. Furthermore, for different pairs of adjacent nodes, the partly transmitted Information can be completely distinct. The constraint on “Partial Information transmission” makes the filtering problem in sensor networks more challenging and practical. Some criteria concerning the connection gains are derived and used to design efficient PIB distributed filter to achieve the following objectives: i) the sensor network can efficiently tract two desired targets in the absence of disturbance and noise; ii) the filter satisfies certain given performance constraint. By using the regrouping method, which is an effective way to derive the main results, some simple yet effective criteria are derived for PIB distributed filtering. A numerical example is utilized to illustrate the effectiveness of the theoretical results.
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Two-targets tracking problem in sensor network with Partial Information transmission
2011Co-Authors: Chi Huang, Wenjun XiongAbstract:In this paper, the sensor network with Partial Information transmission is introduced. Unlike existing sensor network models, the Information transmission between the sensors are assumed to have an imperfect physical condition — only Partial Information can be transmitted. Furthermore, for varying connections, the partly transmitted Information can be completely distinct. The constraint on “Partial Information transmission” makes the tracking problem in sensor network more challenging. This paper deals with the two-targets tracking problem in sensor networks with Partial Information transmission. To reflect a more realistic situation, the interactions are assumed to exist between the targets and each sensor can only detect part of the targets' states. By employing the synchronization theory in complex network, some simple yet effective criteria are derived to guarantee the sensors track to the targets. A numerical example is utilized to illustrate the effectiveness of the proposed results.
Guangchen Wang - One of the best experts on this subject based on the ideXlab platform.
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a Partial Information non zero sum differential game of backward stochastic differential equations with applications
Automatica, 2012Co-Authors: Guangchen WangAbstract:This paper is concerned with a new kind of non-zero sum differential game of backward stochastic differential equations (BSDEs). It is required that the control is adapted to a sub-filtration of the filtration generated by the underlying Brownian motion. We establish a necessary condition in the form of maximum principle with Pontryagin's type for open-loop Nash equilibrium point of this type of Partial Information game, and then give a verification theorem which is a sufficient condition for Nash equilibrium point. The theoretical results are applied to study a Partial Information linear-quadratic (LQ) game and a Partial Information financial problem.
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Maximum Principles for a Class of Partial Information Risk-Sensitive Optimal Controls
IEEE Transactions on Automatic Control, 2010Co-Authors: Jianhui Huang, Guangchen WangAbstract:In this technical note, we study a class of Partial Information risk-sensitive optimal controls. The main results are some necessary and sufficient conditions for these Partial Information control problems. For illustration, one example is proposed and then solved using our results.
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A Maximum Principle for Partial Information Backward Stochastic Control Problems with Applications
SIAM Journal on Control and Optimization, 2009Co-Authors: Jianhui Huang, Guangchen Wang, Jie XiongAbstract:This paper studies the Partial Information control problems of backward stochastic systems. There are three major contributions made in this paper: (i) First, we obtain a new stochastic maximum principle for Partial Information control problems. Our method relies on a direct calculation of the derivative of the cost functional. (ii) Second, we introduce two classes of Partial Information linear-quadratic backward control problems for the first time and then investigate them using the maximum principle. Complete and explicit solutions are obtained in terms of some forward and backward stochastic differential filtering equations. (iii) Last but not least, we study a class of full Information stochastic pension fund optimization problems which can be viewed as a special case of our general Partial Information ones. Applying the aforementioned maximum principle, we derive the optimal contribution policy in closed-form and present some related economic remarks.
Moshe Y Vardi - One of the best experts on this subject based on the ideXlab platform.
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solving Partial Information stochastic parity games
Logic in Computer Science, 2013Co-Authors: Sumit Nain, Moshe Y VardiAbstract:We study one-sided Partial-Information 2-player concurrent stochastic games with parity objectives. In such a game, one of the players has only Partial visibility of the state of the game, while the other player has complete knowledge. In general, such games are known to be undecidable, even for the case of a single player (POMDP). These undecidability results depend crucially on player strategies that exploit an infinite amount of memory. However, in many applications of games, one is usually more interested in finding a finite-memory strategy. We consider the problem of whether the player with Partial Information has a finite-memory winning strategy when the player with complete Information is allowed to use an arbitrary amount of memory. We show that this problem is decidable.
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LICS - Solving Partial-Information Stochastic Parity Games
2013 28th Annual ACM IEEE Symposium on Logic in Computer Science, 2013Co-Authors: Sumit Nain, Moshe Y VardiAbstract:We study one-sided Partial-Information 2-player concurrent stochastic games with parity objectives. In such a game, one of the players has only Partial visibility of the state of the game, while the other player has complete knowledge. In general, such games are known to be undecidable, even for the case of a single player (POMDP). These undecidability results depend crucially on player strategies that exploit an infinite amount of memory. However, in many applications of games, one is usually more interested in finding a finite-memory strategy. We consider the problem of whether the player with Partial Information has a finite-memory winning strategy when the player with complete Information is allowed to use an arbitrary amount of memory. We show that this problem is decidable.
Shai Shalev-shwartz - One of the best experts on this subject based on the ideXlab platform.
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Subspace learning with Partial Information
Journal of Machine Learning Research, 2016Co-Authors: Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-shwartzAbstract:The goal of subspace learning is to find a k-dimensional subspace of Rd, such that the expected squared distance between instance vectors and the subspace is as small as possible. In this paper we study subspace learning in a Partial Information setting, in which the learner can only observe r ≤ d attributes from each instance vector. We propose several efficient algorithms for this task, and analyze their sample complexity.
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Subspace Learning with Partial Information
arXiv: Learning, 2014Co-Authors: Alon Gonen, Dan Rosenbaum, Yonina C. Eldar, Shai Shalev-shwartzAbstract:The goal of subspace learning is to find a $k$-dimensional subspace of $\mathbb{R}^d$, such that the expected squared distance between instance vectors and the subspace is as small as possible. In this paper we study subspace learning in a Partial Information setting, in which the learner can only observe $r \le d$ attributes from each instance vector. We propose several efficient algorithms for this task, and analyze their sample complexity
Alain Bensoussan - One of the best experts on this subject based on the ideXlab platform.
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Stochastic control with Partial Information
[1992] Proceedings of the 31st IEEE Conference on Decision and Control, 1Co-Authors: Alain BensoussanAbstract:The main topics in the area of stochastic control with Partial Information are reviewed. These topics are the linear quadratic Gaussian model; the linear exponential Gaussian model, the Zakai and Kushner equations, the exact solutions of Zakai and Kushner equations: the separation principle; the stochastic maximum principle in the context of Partial Information; dynamic programming in the context of Partial Information; and existence theory. >
