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Battistelli G.,University of Florence | Benavoli A.,Istituto Dalle Molle di Studi sullIntelligenza Artificiale | Chisci L.,University of Florence
Systems and Control Letters | Year: 2012

This paper deals with the problem of estimating the state of a discrete-time linear stochastic dynamical system on the basis of data collected from multiple sensors subject to a limitation on the communication rate from the remote sensor units. The optimal probabilistic measurement-independent strategy for deciding when to transmit estimates from each sensor is derived. Simulation results show that the derived strategy yields certain advantages in terms of worst-case time-averaged performance with respect to periodic strategies when coordination among sensors is not possible. © 2011 Elsevier B.V. All rights reserved. Source


Cozman F.G.,University of Sao Paulo | Polpo De Campos C.,Istituto Dalle Molle di Studi sullIntelligenza Artificiale
International Journal of Approximate Reasoning | Year: 2014

Kuznetsov independence of variables X and Y means that, for any pair of bounded functions f(X) and g(Y), E[f(X)g(Y)]=E[f(X)] E[g(Y)], where E[×] denotes interval-valued expectation and denotes interval multiplication. We present properties of Kuznetsov independence for several variables, and connect it with other concepts of independence in the literature; in particular we show that strong extensions are always included in sets of probability distributions whose lower and upper expectations satisfy Kuznetsov independence. We introduce an algorithm that computes lower expectations subject to judgments of Kuznetsov independence by mixing column generation techniques with nonlinear programming. Finally, we define a concept of conditional Kuznetsov independence, and study its graphoid properties. © 2013 Elsevier Inc. All rights reserved. Source


Corani G.,Istituto Dalle Molle di Studi sullIntelligenza Artificiale | Mignatti A.,Polytechnic of Milan
International Journal of Approximate Reasoning | Year: 2015

Bayesian model averaging (BMA) is the state of the art approach for overcoming model uncertainty. Yet, especially on small data sets, the results yielded by BMA might be sensitive to the prior over the models. Credal model averaging (CMA) addresses this problem by substituting the single prior over the models by a set of priors (credal set). Such approach solves the problem of how to choose the prior over the models and automates sensitivity analysis. We discuss various CMA algorithms for building an ensemble of logistic regressors characterized by different sets of covariates. We show how CMA can be appropriately tuned to the case in which one is prior-ignorant and to the case in which instead domain knowledge is available. CMA detects prior-dependent instances, namely instances in which a different class is more probable depending on the prior over the models. On such instances CMA suspends the judgment, returning multiple classes. We thoroughly compare different BMA and CMA variants on a real case study, predicting presence of Alpine marmot burrows in an Alpine valley. We find that BMA is almost a random guesser on the instances recognized as prior-dependent by CMA. © 2014 Elsevier Inc. All rights reserved. Source


Piga D.,Istituto Dalle Molle di Studi sullIntelligenza Artificiale | Toth R.,TU Eindhoven
Automatica | Year: 2014

Parametric identification of linear time-invariant (LTI) systems with output-error (OE) type of noise model structures has a well-established theoretical framework. Different algorithms, like instrumental-variables based approaches or prediction error methods (PEMs), have been proposed in the literature to compute a consistent parameter estimate for linear OE systems. Although the prediction error method provides a consistent parameter estimate also for nonlinear output-error (NOE) systems, it requires to compute the solution of a nonconvex optimization problem. Therefore, an accurate initialization of the numerical optimization algorithms is required, otherwise they may get stuck in a local minimum and, as a consequence, the computed estimate of the system might not be accurate. In this paper, we propose an approach to obtain, in a computationally efficient fashion, a consistent parameter estimate for output-error systems with polynomial nonlinearities. The performance of the method is demonstrated through a simulation example. © 2014 Elsevier Ltd. All rights reserved. Source


Battistelli G.,University of Florence | Benavoli A.,Istituto Dalle Molle di Studi sullIntelligenza Artificiale | Chisci L.,University of Florence
Automatica | Year: 2012

This paper deals with the problem of estimating the state of a discrete-time linear stochastic dynamical system on the basis of data collected from multiple sensors subject to a limitation on the communication rate from the sensors. More specifically, the attention is devoted to a centralized sensor network consisting of: (1) multiple remote nodes which collect measurements of the given system, compute state estimates at the full measurement rate and transmit data (either raw measurements or estimates) at a reduced communication rate; (2) a fusion node that, based on received data, provides an estimate of the system state at the full rate. Local data-driven transmission strategies are considered and issues related to the stability and performance of such strategies are investigated. Simulation results confirm the effectiveness of the proposed strategies. © 2012 Elsevier Ltd. All rights reserved. Source

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