This paper investigates the notion of stability for Stochastic Hybrid Systems. The uncertainty is introduced in the discrete jumps between the domains, as if we had an underlying Markov Chain. The jumps happen every fixed time T ; moreover, a result is given for the case of probabilistic dwelling times inside each domain. Unlike the more classical Hybrid Systems setting, the guards here are tim...

Imprecise-information processing is an important component of artificial intelligence (including computational intelligence). With the development of information and intelligence sciences and technologies as well as the rise in social requirements, imprecise-information processing is becoming more and more important and urgent, and it will play an indispensable role in intelligent systems, espe...

Chemical process engineering and machine learning are merging rapidly, hybrid models have shown promising results in analysis design. However, uncertainties first-principles an adverse effect on extrapolations inferences based models. Parameter sensitivities essential tool to understand better the underlying uncertainty propagation system identification challenges. Still, standard parameter sen...

Energy management strategy (EMS) is important to ensure energy-saving performance of hybrid electric vehicle (HEV). However, the power coupling property between different sources, together with stochastic demand fluctuation poses great challenges for EMS achieve desirable in real-world scenario. This paper presents an uncertainty-aware energy HEV. A speed predictor combining convolutional neura...

Due to the recent technological advances, real-time hybrid dynamical systems are becoming ubiquitous. Most of these systems behave unpredictably, and thus, exhibit uncertainty. Hence, a formal framework to model systems with unpredictable behaviours is needed. We develop Probabilistic Constraint Nets (PCN), a new framework that can handle a wide range of uncertainty, whether it be probabilistic...

Nicola Pedroni, E Zio, A Pasanisi, M Couplet. Empirical comparison of two methods for the Bayesian update of the parameters of probability distributions in a two-level hybrid probabilistic-possibilistic uncertainty framework for risk assessment. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, American Society of Civil Engineers (ASCE), 2015, pp.04015...

In this paper, we advance a recently-proposed uncertainty decoding scheme for DNN-HMM (deep neural network hidden Markov model) hybrid systems. This numerical sampling concept averages DNN outputs produced by a finite set of feature samples (drawn from a probabilistic distortion model) to approximate the posterior likelihoods of the context-dependent HMM states. As main innovation, we propose a...