Effect of time correlation of input patterns on the convergence of on-line learning

We studied the effects of time correlation of subsequent patterns on the convergence of on-line learning by a feedforward neural network with the backpropagation algorithm. By using a chaotic time series as sequences of correlated patterns, we found that the unexpected scaling of converging time with the learning parameter emerges when time-correlated patterns accelerate the learning process.