We discuss the problem of overfitting of probabilistic neural networks in the framework of statistical pattern recognition. The probabilistic approach to neural networks provides a...
This paper deals with a category of concavifiable functions that can be used to model inelastic traffic in the network. Such class of functions can be concavified within an interva...
We propose heuristic and exact algorithms for the (periodic and non-periodic) train timetabling problem on a corridor that are based on the solution of the LP relaxation of an ILP...
This paper deals with the job-shop scheduling problem with sequencedependent setup times. We propose a new method to solve the makespan minimization problem to optimality. The met...
— In this paper, we present a stable receding horizon model predictive control for discrete-time nonlinear systems. The standard MPC scheme is modified to incorporate (1) a bloc...
Jing Sun, Ilya V. Kolmanovsky, Reza Ghaemi, Shuhao...