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Learning extremal regulator implementation by a stochastic automaton and stochastic approximation theory

Ivan Brůha (1980)

Aplikace matematiky

There exist many different approaches to the investigation of the characteristics of learning system. These approaches use different branches of mathematics and, thus, obtain different results, some of them are too complicated and others do not match the results of practical experiments. This paper presents the modelling of learning systems by means of stochastic automate, mainly one particular model of a learning extremal regulator. The proof of convergence is based on Dvoretzky's Theorem on stochastic...

Learning the naive Bayes classifier with optimization models

Sona Taheri, Musa Mammadov (2013)

International Journal of Applied Mathematics and Computer Science

Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three novel optimization...

Limiting average cost control problems in a class of discrete-time stochastic systems

Nadine Hilgert, Onesimo Hernández-Lerma (2001)

Applicationes Mathematicae

We consider a class of ℝ d -valued stochastic control systems, with possibly unbounded costs. The systems evolve according to a discrete-time equation x t + 1 = G ₙ ( x t , a t ) + ξ t (t = 0,1,... ), for each fixed n = 0,1,..., where the ξ t are i.i.d. random vectors, and the Gₙ are given functions converging pointwise to some function G ∞ as n → ∞. Under suitable hypotheses, our main results state the existence of stationary control policies that are expected average cost (EAC) optimal and sample path average cost (SPAC) optimal for...

LMI optimization problem of delay-dependent robust stability criteria for stochastic systems with polytopic and linear fractional uncertainties

Pagavathigounder Balasubramaniam, Shanmugam Lakshmanan, Rajan Rakkiyappan (2012)

International Journal of Applied Mathematics and Computer Science

This paper studies an LMI optimization problem of delay-dependent robust stability criteria for stochastic systems with polytopic and linear fractional uncertainties. The delay is assumed to be time-varying and belong to a given interval, which means that lower and upper bounds of this interval time-varying delay are available. The uncertainty under consideration includes polytopic-type uncertainty and linear fractional norm-bounded uncertainty. Based on the new Lyapunov-Krasovskii functional, some...

Local detection of defects from image sequences

Ewaryst Rafajłowicz, Marek Wnuk, Wojciech Rafajłowicz (2008)

International Journal of Applied Mathematics and Computer Science

Our aim is to discuss three approaches to the detection of defects in continuous production processes, which are based on local methods of processing image sequences. These approaches are motivated by and applicable to images of hot metals or other surfaces, which are uniform at a macroscopic level, when defects are not present. The first of them is based on the estimation of fractal dimensions of image cross-sections. The second and third approaches are compositions of known techniques, which are...

LPV design of fault-tolerant control for road vehicles

Péter Gáspár, Zoltán Szabó, József Bokor (2012)

International Journal of Applied Mathematics and Computer Science

The aim of the paper is to present a supervisory decentralized architecture for the design and development of reconfigurable and fault-tolerant control systems in road vehicles. The performance specifications are guaranteed by local controllers, while the coordination of these components is provided by a supervisor. Since the monitoring components and FDI filters provide the supervisor with information about the various vehicle maneuvers and the different fault operations, it is able to make decisions...

Malliavin method for optimal investment in financial markets with memory

Qiguang An, Guoqing Zhao, Gaofeng Zong (2016)

Open Mathematics

We consider a financial market with memory effects in which wealth processes are driven by mean-field stochastic Volterra equations. In this financial market, the classical dynamic programming method can not be used to study the optimal investment problem, because the solution of mean-field stochastic Volterra equation is not a Markov process. In this paper, a new method through Malliavin calculus introduced in [1], can be used to obtain the optimal investment in a Volterra type financial market....

Marginal problem, statistical estimation, and Möbius formula

Martin Janžura (2007)

Kybernetika

A solution to the marginal problem is obtained in a form of parametric exponential (Gibbs–Markov) distribution, where the unknown parameters are obtained by an optimization procedure that agrees with the maximum likelihood (ML) estimate. With respect to a difficult performance of the method we propose also an alternative approach, providing the original basis of marginals can be appropriately extended. Then the (numerically feasible) solution can be obtained either by the maximum pseudo-likelihood...

Markov decision processes with time-varying discount factors and random horizon

Rocio Ilhuicatzi-Roldán, Hugo Cruz-Suárez, Selene Chávez-Rodríguez (2017)

Kybernetika

This paper is related to Markov Decision Processes. The optimal control problem is to minimize the expected total discounted cost, with a non-constant discount factor. The discount factor is time-varying and it could depend on the state and the action. Furthermore, it is considered that the horizon of the optimization problem is given by a discrete random variable, that is, a random horizon is assumed. Under general conditions on Markov control model, using the dynamic programming approach, an optimality...

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