Minimal disturbance back-propagation algorithm

Summary form only given, as follows. A novel learning algorithm for multilayered neural networks is presented. This algorithm, called minimal disturbance backpropagation, approximates a least mean squared error minimization of the error function while minimally disturbing the connection weights in the network. This means that the information previously trained into the network is disturbed to the smallest amount possible while achieving the desired error correction. Simulation results indicate that this algorithm is more robust and yields much faster convergence rates than the standard backpropagation algorithm.