A constrained optimization algorithm for training locally recurrent globally feedforward neural networks

This paper presents a novel learning algorithm for training locally recurrent globally feedforward neural networks. The training task is formulated as a constrained optimization problem, whose objective is twofold: (i) minimization of an error measure, leading to successful approximation of the input/output mapping and (ii) optimization of an additional functional, which aims at accelerating the learning process. Simulation results on a benchmark identification problem demonstrate that, compared to other learning schemes, the proposed algorithm has enhanced qualities, including improved speed of convergence, accuracy and robustness.