This paper addresses the problems of stabilization and by means of state feedback parameter-dependent gains applied to discrete-time linear systems whose matrices are affected by arbitrarily time-varying parameters belonging to a polytope. The…
Papers in Control and Optimization
Control and Optimization papers in Mathematics, G
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Gain scheduling controllers are considered in this paper. The gain scheduling problem where the scheduling parameter vector θ cannot be measured directly, but needs to be estimated, is considered. An estimation of θ has been derived by…
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This paper presents a gain-scheduled design for a missile longitudinal autopilot. The gain-scheduled design is novel in that it does not involve linearizations about trim conditions of the missile dynamics. Rather, the missile dynamics are brought…
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This paper presents a gain-scheduling of minimax optimal controllers for a general class of uncertain linear parameter-varying (LPV) systems. The proposed gain-scheduled controller consists of a set of minimax optimal controllers designed for…
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Increased reliance on computational approaches in the life sciences has revealed grave concerns about how accessible and reproducible computation-reliant results truly are. Galaxy http://usegalaxy.org, an open web-based platform for genomic…
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Anti-derivatives of wavelets are used for the numerical solution of differential equations. Optimal error estimates are obtained in the applications to two-point boundary value problems of second order. The orthogonal property of the wavelets is…
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Classical Galois theory is a subject generally acknowledged to be one of the most central and beautiful areas in pure mathematics. This text develops the subject systematically and from the beginning, requiring of the reader only basic facts about…
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The paper presents a new class of hybrid circuits named GALS-SA (Globally Asynchronous Locally Synchronous - Structured ASIC) that incorporates the advantages of GALS (Globally Asynchronous Locally Synchronous) architecture together with the ones of…
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Multiple access methods in a wireless network allow multiple nodes to share a set of available channels for data transmission. The nodes can either compete or cooperate with each other to access the channel(s) so that either an individual or a group…
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We study the close connections between game theory, on-line prediction and boosting. After a brief review of game theory, we describe an algorithm for learning to play repeated games based on the on-line prediction methods of Littlestone and…
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We consider the problem of Internet switching, where traffic is generated by selfish users. We study a packetized (TCP-like) traffic model, which is more realistic than the widely used fluid model. We assume that routers have First-In-First-Out…
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On October 2006 REE (Red Electrica de Espantildea), one of Gamesapsilas major markets, publishes a new code version with new requirements regarding voltage ride through capabilities. All new wind turbines installed from January 2008 on must fulfill…
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Frequency hopping has been the most popularly considered approach for alleviating the effects of jamming attacks. In this paper, we provide a novel, measurement-driven, game theoretic framework that captures the interactions between a communication…
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Genetic algorithms (GA) and radial basis function (RBF) neural network are combined in this paper. Prediction model of gas content in coal seam is set up based on GA-RBF neural network optimized by genetic algorithm in network structure and…
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We develop and analyze real-time and accurate filters for nonlinear filtering problems based on the Gaussian distributions. We present the systematic formulation of Gaussian filters and develop efficient and accurate numerical integration of the…
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Sequential Bayesian estimation for dynamic state space models involves recursive estimation of hidden states based on noisy observations. The update of filtering and predictive densities for nonlinear models with non-Gaussian noise using Monte Carlo…
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Computer codes are used in scientific research to study and predict the behaviour of complex systems. Their run times often make uncertainty and sensitivity analyses impractical because of the thousands of runs that are conventionally required, so…
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In this paper we introduce a new underlying probabilistic model for prin- cipal component analysis (PCA). Our formulation interprets PCA as a particular Gaussian process prior on a mapping from a latent space to the observed data-space. We show that…
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In this article, we extend the application of the Gaussian processes technique to classification quantitative structure-activity relationship modeling problems. We explore two approaches, an intrinsic Gaussian processes classification technique and…
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We introduce a novel Bayesian approach to global optimiza- tion using Gaussian processes. We frame the optimization of both noisy and noiseless functions as sequential decision problems, and introduce myopic and non-myopic solutions to them. Here…
Papers in associated sub-disciplines
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