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This paper is concerned with the filtering problem for a class of nonlinear systems with stochastic sensor saturations and event-triggered measurement transmissions. An event-triggered transmission scheme is proposed with hope to ease the traffic burden and improve the energy efficiency. The measurements are subject to randomly occurring sensor saturations governed by Bernoulli distributed sequences. Special effort is made to obtain an upper bound of the filtering error covariance in the presence of linearization errors, stochastic sensor saturations as well as event-triggered transmissions. A filter is designed to minimize the obtained upper bound at each time step by solving two sets of Riccati-like matrix equations, and thus the recursive algorithm is suitable for online computation. Sufficient conditions are established under which the filtering error is exponentially bounded in mean square. The applicability of the presented method is demonstrated by dealing with the fault estimation problem. An illustrative example is exploited to show the effectiveness of the proposed algorithm.

This paper presents a first-order distributed continuous-time algorithm for computing the leastsquares solution to a linear equation over networks. Given the uniqueness of the solution, with nonintegrable and diminishing step size, convergence results are provided for fixed graphs. The exact rate of convergence is also established for various types of step size choices falling into that category. For the case where non-unique solutions exist, convergence to one such solution is proved for constantly connected switching graphs with square integrable step size, and for uniformly jointly connected switching graphs under the boundedness assumption on system states. Validation of the results and illustration of the impact of step size on the convergence speed are made using a few numerical examples. * A preliminary version [11] of this work was presented at the 56th IEEE Conference on Decision and Control, December 12-15, 2017 in Melbourne, Australia.network only has access to one or a few of the individual linear equations making up the full system due to security issues or memory limitation, and is only permitted to interact with a subset of the other agents. A number of contributions have been made to the development of distributed solvable linear equation solvers, where simple first-order distributed algorithms, in continuous-time or discrete-time [1,8,9,12,15,16,23,27,30], manage to deliver satisfactory solutions even for switching network structures.As is known to all, however, another frequent case in practical problems is concerned with non-solvable linear equations, in which we often seek a least-squares solution by minimizing the associated objective function.However, it seems a rather challenging problem in developing distributed least-squares solvers for network linear equations, due to the mismatch between individual linear equations at each node and the network least-squares solution. Despite the difficulties, there exist a few distributed algorithms developed for the least-squares problem using different approaches, such as second-order algorithms [3,10,28,29], state expansion [16] and the high gain consensus gain method [23]. Second-order distributed least-squares solvers [3,10,28,29] generally can produce good convergence performance, however, they rely on restricted network structures and demand higher communication and storage capacities. The state expansion method [16] is based on enlarging the state dimension and then applying the existing methods for linear equations with exact solutions directly, but a negative feature is that the nodes must have access to more knowledge than their own linear equations. It was shown in [23] that first-order algorithms for exact solutions can be adapted to the least-squares case by a high consensus gain, but only in an approximate sense.In this paper, we propose a first-order continuous-time flow for the least-squares problems of network linear equations, in which each agent keeps averaging the state with its neighbors' and at the same time descends along the neg...

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Geographically weighted regression (GWR) models handle geographical dependence through a spatially varying coefficient model and have been widely used in applied science, but its Bayesian extension is unclear because it involves a weighted log-likelihood which does not imply a probability distribution on data. We present a Bayesian GWR model and show that its essence is dealing with partial misspecification of the model. Current modularized Bayesian inference methods accommodate partial misspecification from single component of the model. We extend these methods to handle partial misspecification in more than one component of the model, as required for our Bayesian GWR model. Information from the various spatial locations is manipulated via a geographically weighted kernel and the optimal manipulation is chosen according to a Kullback-Leibler (KL) divergence. We justify the model via an information risk minimization approach and show the consistency of the proposed estimator in terms of a geographically weighted KL divergence.