In this work we address the problem of inferring the height of atmospheric boundary layer from lidar data. From one hand the problem to reconstruct the boundary layer dynamics is addressed using a Bayesian statistical inference method. Both parameter estimation and classi cation to mixed/residual layer, are studied. Probabilistic speci cation of the unknown variables is deduced from measurements. Hierarchical Bayesian models are adopted to relax the prior assumptions on the unknowns. Markov chain Monte Carlo (MCMC) simulations are conducted to explore the high dimensional posterior state space. On the other hand a novel neuro-fuzzy model (Fuzzy Relational Neural Network) is used to obtain an IF-THEN reasoning scheme able to classify future observations. Experiments on real data are introduced.
Statistical and Fuzzy Approaches for Atmospheric Boundary Layer Classification
CIARAMELLA, Angelo;RICCIO, Angelo;
2009-01-01
Abstract
In this work we address the problem of inferring the height of atmospheric boundary layer from lidar data. From one hand the problem to reconstruct the boundary layer dynamics is addressed using a Bayesian statistical inference method. Both parameter estimation and classi cation to mixed/residual layer, are studied. Probabilistic speci cation of the unknown variables is deduced from measurements. Hierarchical Bayesian models are adopted to relax the prior assumptions on the unknowns. Markov chain Monte Carlo (MCMC) simulations are conducted to explore the high dimensional posterior state space. On the other hand a novel neuro-fuzzy model (Fuzzy Relational Neural Network) is used to obtain an IF-THEN reasoning scheme able to classify future observations. Experiments on real data are introduced.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
