Neural networks for estimating conditional distributions and their associated quantiles are investigated in this paper. A basic network structure is developed on the basis of kernel estimation theory, and consistency is proved from a mild set of assumptions. A number of applications within statistics, decision theory, and signal processing are suggested, and a numerical example illustrating the capabilities of the elaborated network is given.
This paper presents a graphical integrated modelling and performance-analysis tool based on deterministic network calculus (DNC) and implemented as an open source toolbox for the MATLAB/SimuLink environment. The paper introduces briefly the main concepts from network calculus and especially recent results for systems with cyclic dependencies, which appear in cases of cyclic data/work flow or counter directional resource and work flows. A number of network element types are supported including various arbitration/scheduling disciplines such as: Fixed Priorities, FIFO, TDMA, round robin/token passing and EDF along with packetization, flow control and flow convergence. These are all presented in the paper together with auxiliary tools like worst case backlog and delay calculations. Implementation details of general interest are presented along with illustrative examples demonstrating the virtues of the separate modelling elements and the overall tool framework. Discussion is provided concerning issues in system stability and the ability of DNC to provide usefull estimates of stability limits. Likewise current activities to support synchronous communication and flow control within the tool are presented.
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