Optimized Learning with Bounded Error for Feedforward Neural Networks
A. Alessandri, Marcello Sanguineti, Manfredi Maggiore · 2002
A learning algorithm for feedforward neural networks is presented that is based on a parameter estimation approach. The algorithm is particularly well-suited for batch learning and allows one to deal with large data sets in a computationally efficient way. An analysis of its convergence and robustness properties is made. Simulation results confirm the effectiveness of the algorithm and its advantages over learning based on backpropagation and extended Kalman filter.