This article deals with the problem of H and l-l filtering for discrete-time Takagi-Sugeno fuzzy nonhomogeneous Markov jump systems with quantization effects, respectively. The time-varying transition probabilities are in a polytope set. To reduce conservativeness, a mode-dependent logarithmic quantizer is considered in this article. Based on the fuzzy-rule-dependent Lyapunov function, sufficient conditions are given such that the filtering error system is stochastically stable and has a prescribed H or l-l performance index, respectively. Finally, a practical example is provided to illustrate the effectiveness of the proposed fuzzy filter design methods.
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http://dx.doi.org/10.1109/TCYB.2020.2991159 | DOI Listing |
Math Biosci
December 2024
Department of Mathematics, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece. Electronic address:
In the realm of epidemiology, it is essential to accurately assess epidemic phenomena through the adoption of innovative techniques that yield reliable predictions. This article introduces an advanced method that merges the Extended Kalman Filter approach with recursive algorithms to compute critical stochastic attributes important for evaluating epidemics. A new three-dimensional discrete Markov process is presented, according to which the total number of infections, deaths, and the duration of epidemic outbreaks are estimated.
View Article and Find Full Text PDFThis study addresses a tampered-data recovery problem for linear discrete-time systems with completely unknown system dynamics under stealthy attacks. The basic idea is to identify the stealthy attack, that lies in any of attack-stealthy subspaces, and compensate for it. Different from the existing sparse recovery methods which are applicable to nonstealthy sparse attacks, a novel encoding scheme, where a set of subdecoding matrices is designed specifically for each 1-D attack-stealthy subspace, is developed so that the parameters of the stealthy attack can be identified via a subspace projection technique.
View Article and Find Full Text PDFJ Comput Biol
December 2024
ESOMAS Department, University of Torino and Collegio Carlo Alberto, Torino, Italy.
Fleming-Viot diffusions are widely used stochastic models for population dynamics that extend the celebrated Wright-Fisher diffusions. They describe the temporal evolution of the relative frequencies of the allelic types in an ideally infinite panmictic population, whose individuals undergo random genetic drift and at birth can mutate to a new allelic type drawn from a possibly infinite potential pool, independently of their parent. Recently, Bayesian nonparametric inference has been considered for this model when a finite sample of individuals is drawn from the population at several discrete time points.
View Article and Find Full Text PDFTo enhance system robustness in the face of uncertainty and achieve adaptive optimization of control strategies, a novel algorithm based on the unscented Kalman filter (UKF) is developed. This algorithm addresses the finite-horizon optimal tracking control problem (FHOTCP) for nonlinear discrete-time (DT) systems with uncertainty and asymmetric input constraints. An augmented system is constructed with asymmetric control constraints being considered.
View Article and Find Full Text PDFPLoS One
September 2024
Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, United States of America.
We develop a general framework for state estimation in systems modeled with noise-polluted continuous time dynamics and discrete time noisy measurements. Our approach is based on maximum likelihood estimation and employs the calculus of variations to derive optimality conditions for continuous time functions. We make no prior assumptions on the form of the mapping from measurements to state-estimate or on the distributions of the noise terms, making the framework more general than Kalman filtering/smoothing where this mapping is assumed to be linear and the noises Gaussian.
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