Research on vehicle longitudinal control with a stop and go system is presently one of the most important topics in the field of intelligent transportation systems. The purpose of stop and go systems is to assist drivers for repeatedly accelerate and stop their vehicles in traffic jams. This system can improve the driving comfort, safety and reduce the danger of collisions and fuel consumption....

This contribution comments on the advances of the latest Crystal Structure Prediction blind test and the challenges still lying ahead.

PM10 constitutes a major concern for Milan air quality. We presents a series of results obtained applying different neural networks approaches to the PM10 prediction problem. The 1-day ahead prediction shows a satisfactory level of accuracy, which may be further improved if a proper deseasonalization approach is adopted, thus transferring some a priori knowledge in the data pre-processing step....

While tropical cyclone (TC) prediction, in particular TC genesis, remains very challenging, accurate prediction of TCs is critical for timely preparedness andmitigation. Using a new version of theGeophysical Fluid Dynamics Laboratory (GFDL) coupled model, the authors studied the predictability of two destructive landfall TCs: Hurricane Sandy in 2012 and Super Typhoon Haiyan in 2013. Results dem...

Amaximization of the expected entropy of the predictive distribution interpretation of Akaike's minimum AIC procedure is exploited for the modeling and prediction of time series with trend and seasonal mean value functions and stationary covariances. The AIC criterion best one-step-ahead and best twelvestep-ahead prediction models are different. They exhibit the relative optimality properties f...

We compare the predication performance of neural networks with the different frequencies of input data, namely daily data, weekly data, monthly data. In the 1 day and 1 week ahead prediction of foreign exchange rates forecasting, the neural networks with the weekly input data performs better than the random walk models. In the 1 month ahead prediction of foreign exchange rates forecasting, only...

We consider the problem of multistep-ahead prediction in time series analysis by using nonparametric smoothing techniques. Forecasting is always one of the main objectives in time series analysis. Research has shown that non-linear time series models have certain advantages in multistep-ahead forecasting. Traditionally, nonparametric k-step-ahead least squares prediction for non-linear autoregr...

In this paper, multi step ahead prediction of monthly sunspot real time series are carried out. This series is highly chaotic in nature [7]. This paper compares performance of proposed Jordan Elman Neural Network with TLRNN (Time lag recurrent neural network), and RNN (Recurrent neural network) for multi-step ahead (1, 6, 12, 18, 24) predictions. It is seen that the proposed neural network mode...

Traditional wafer lot output time prediction methods are based on the historical data of the fab. However, the influence of the future release plan has been neglected. In addition, a lot that will be released in the future might appear in front of another lot that currently exists in the fab. For these reasons, to further improve the accuracy of wafer lot output time prediction, the future rele...