This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear un...
W. D. Wan Rosli, Z. Zainuddin, R. Lanouette, S. Sa...
Existing Recurrent Neural Networks (RNNs) are limited in their ability to model dynamical systems with nonlinearities and hidden internal states. Here we use our general framework...
This paper presents an study about a new Hybrid method GRASPES - for time series prediction, inspired in F. Takens theorem and based on a multi-start metaheuristic for combinatori...
Aranildo Rodrigues Lima Junior, Tiago Alessandro E...
A two-neural network approach to solving nonlinear optimal control problems is described in this study. This approach called the adaptive critic method consists of one neural netw...
We suggest a recurrent neural network (RNN) model with a recurrent part corresponding to iterative function systems (IFS) introduced by Barnsley 1] as a fractal image compression ...