Optimization-based learning with bounded error for feedforward neural networks

An optimization-based learning algorithm for feedforward neural networks is presented, in which the network weights are determined by minimizing a sliding-window cost. 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.