71 research outputs found
Recursive parameter estimation of regression model when the interval of possible values is given
Towards probabilistic synchronization of local controllers
The traditional use of global and centralised control methods, fails for large, complex, noisy and highly connected systems, which typify many real world industrial and commercial systems. This paper provides an efficient bottom up design of distributed control in which many simple components communicate and cooperate to achieve a joint system goal. Each component acts individually so as to maximise personal utility whilst obtaining probabilistic information on the global system merely through local message-passing. This leads to an implied scalable and collective control strategy for complex dynamical systems, without the problems of global centralised control. Robustness is addressed by employing a fully probabilistic design, which can cope with inherent uncertainties, can be implemented adaptively and opens a systematic rich way to information sharing. This paper opens the foreseen direction and inspects the proposed design on a linearised version of coupled map lattice with spatiotemporal chaos. A version close to linear quadratic design gives an initial insight into possible behaviours of such networks
Scalable harmonization of complex networks with local adaptive controllers
Computational and communication complexities call for distributed, robust, and adaptive control. This paper proposes a promising way of bottom-up design of distributed control in which simple controllers are responsible for individual nodes. The overall behavior of the network can be achieved by interconnecting such controlled loops in cascade control for example and by enabling the individual nodes to share information about data with their neighbors without aiming at unattainable global solution. The problem is addressed by employing a fully probabilistic design, which can cope with inherent uncertainties, that can be implemented adaptively and which provide a systematic rich way to information sharing. This paper elaborates the overall solution, applies it to linear-Gaussian case, and provides simulation results
Fully probabilistic control design in an adaptive critic framework
Optimal stochastic controller pushes the closed-loop behavior as close as possible to the desired one. The fully probabilistic design (FPD) uses probabilistic description of the desired closed loop and minimizes Kullback-Leibler divergence of the closed-loop description to the desired one. Practical exploitation of the fully probabilistic design control theory continues to be hindered by the computational complexities involved in numerically solving the associated stochastic dynamic programming problem. In particular very hard multivariate integration and an approximate interpolation of the involved multivariate functions. This paper proposes a new fully probabilistic contro algorithm that uses the adaptive critic methods to circumvent the need for explicitly evaluating the optimal value function, thereby dramatically reducing computational requirements. This is a main contribution of this short paper
One-sided approximation of Bayes rule and its application to regression model with Cauchy noise
Decision-theoretical formulation of the calibration problem
The choice of calibration policy is of basic importance in analytical
chemistry. A prototype of the practical calibration problem is
formulated as a mathematical task and a Bayesian solution of the
resulting decision problem is presented. The optimum feedback
calibration policy can then be found by dynamic programming. The
underlying parameter estimation and filtering are solved by
updating relevant conditional distributions. In this way: the
necessary information is specified (for instance, the need for
knowledge of the probability distribution of unknown samples is
clearly recognized as the conceptually unavoidable informational
source); the relationship of the information gained from a
calibration experiment to the ultimate goal of calibration, i.e., to
the estimation of unknown samples, is explained; an ideal solution
is given which can serve for comparing various ways of calibration;
and a consistent and conceptually simple guideline is given for
using decision theory when solving problems of analytical chemistry
containing uncertain data. The abstract formulation is systematically
illustrated by an example taken from gas chromatography
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