… network models for predicting intercity freight movements that have appeared in the literature,
and it provides a typology of such models. … models, spatial price equilibrium, and the use of …

… This paper presents an approach for the constrained non-linear predictive control … network
model. Using the resulting linear system in the formulation of the original non-linear predictive …

… connectionist neural network models appear to be most suitable and robust, provided that
a predictive variable reflecting these time-dependent dynamics included in the model either …

… -type models producing a continuous prediction are powerful to rank-… predictive in silico
models using machine learning techniques. For novel chemical series, “global” in silico models …

… In this paper, we apply two recurrent neural network models for MPC based on linear and
quadratic programming formulations. Both neural networks have good convergence …

… was that a network model could be … predictor of the outcome of injury. The ISS derived from
the ICD-9 codes was significantly associated with survival after injury, although its predictive …

… LMS based model. We assess the training as well prediction efficiency of these models for
… The accuracy of conversion of these models is compared using known test patterns. The …

… The performance of these methods have to be evaluated over greater prediction horizons,
where … Finally, neural network models have to be compared also with conventional models. …

… The appropriate software tool to use will depend on the nature of the network model and
objectives of the analysis. For asynchronous simulations and in silico molecular perturbation …

… In order to be able to determine the PEVJ predictive distribution, in Section 3.5 we show how
to compute Jeffreys’ prior for a given Bayesian network model. In Section 4 we see that, for …