Designers have invested much effort in developing accurate branch predictors with short learning periods. Such techniques rely on exploiting complex and relatively large structure...
Recurrent neural networks serve as black-box models for nonlinear dynamical systems identification and time series prediction. Training of recurrent networks typically minimizes t...
We propose a learning algorithm for a variable memory length Markov process. Human communication, whether given as text, handwriting, or speech, has multi characteristic time scal...
It is desirable to ensure that the thermal comfort conditions in offices are in line with the preferences of occupants. Controlling their offices correctly therefore requires the c...
— This paper deals with the tracking problem for constrained linear systems using a model predictive control (MPC) law. As it is well known, MPC provides a control law suitable f...