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Forecasting road traffic conditions requires an accurate knowledge of the spatio-temporal dependencies of traffic flow in transportation networks. In this article, a Bayesian network framework is introduced to model the correlation structure of highway networks in the context of traffic forecast. We formulate the dependency learning problem as an optimization problem and propose an efficient algorithm...
This paper expounds three kinds of grey neural network combined model for short-term prediction of urban traffic speed, and confirms their validity and feasibility by conducting experiment in Beijing road of Jingzhou. Three kinds of networks are parallel grey neural network, series grey neural network, and inlaid grey neural network. The experiment proves that the three kinds of modes are feasible...
In this paper we discuss performance results of a novel real-time closed-loop power amplifier (PA) linearization technique that has originally been proposed by Ahmed and Li. The novel approach performs on-the-fly prediction and measurement of the PA AM/AM and AM/PM non-linearity, stores such non-linear characteristics and calculates their inverse functions in order to pre-distort the base-band amplitude...
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