Reachability analysis in stochastic directed graphs by reinforcement learning

We characterize the reachability probabilities in stochastic directed graphs by means of reinforcement learning methods. In particular, we show that the dynamics of the transition probabilities in a stochastic digraph can be modeled via a difference inclusion, which, in turn, can be interpreted as a Markov decision process. Using the latter framework, we offer a methodology to design reward functions to provide upper and lower bounds on the reachability probabilities of a set of nodes for stochastic digraphs. The effectiveness of the proposed technique is demonstrated by application to the diffusion of epidemic diseases over time-varying contact networks generated by the proximity patterns of mobile agents

Design of controllers for hybrid linear systems with impulsive inputs and periodic jumps

In this study, the problem of designing a controller for a hybrid system with impulsive input and periodic jumps is addressed. In particular, it is shown that any hybrid system with impulsive inputs and periodic jumps can be recast into a discrete-time, linear, time-invariant system, which, in turn, can be used to design a controller by using classical methods. Furthermore, it is shown that, once such a controller has been designed, it can be readily used to control the hybrid system by mean of an interfacing system that is based just on the continuous-time dynamics of the plant to be controlled. Several examples, spanning from aerospace to biomedical applications, are reported in order to corroborate the theoretical results

Citokininek, a növényi in vitro fejlődés kulcsregulátorai

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Overview of the FTU results

Since the 2016 IAEA Fusion Energy Conference, FTU operations have been mainly devoted to experiments on runaway electrons and investigations into a tin liquid limiter; other experiments have involved studies of elongated plasmas and dust. The tearing mode onset in the high density regime has been studied by means of the linear resistive code MARS, and the highly collisional regimes have been investigated. New diagnostics, such as a runaway electron imaging spectroscopy system for in-flight runaway studies and a triple Cherenkov probe for the measurement of escaping electrons, have been successfully installed and tested, and new capabilities of the collective Thomson scattering and the laser induced breakdown spectroscopy diagnostics have been explored

Value iteration via output feedback for LQ optimal control of SISO systems

In this paper, a value iteration algorithm, which makes use of just input/output measurements, is proposed to solve linear quadratic (LQ) optimal control problems for single-input, single-output (SISO) plants. This algorithm is designed by coupling an adaptive Luenberger observer with an indirect value iteration architecture for continuous-time plants. The effectiveness of the proposed approach is validated via numerical simulations

Value iteration for linear quadratic optimal control of single-input single-output systems via output feedback

A value iteration approach based solely on input/output measurements is proposed to solve linear quadratic (LQ) optimal control problems for single-input, single-output (SISO) continuous-time systems. Such an algorithm is designed by coupling an adaptive Luenberger observer with an indirect value iteration architecture. The continuous-time implementation of this controller requires that the gathered estimates are strongly controllable. A hybrid adaptation mechanism is envisioned to overcome such a requirement. The effectiveness of the proposed approach is validated via numerical simulations