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 backpropagation algorithm. By using chaotic time series as sequences of correlated patterns, we found that the unexpected scaling of converging time with learning parameter emerges when time-correlated patterns accelerate learning process. 
Tsuyoshi Hondou, Mitsuaki Yamamoto, Yasuji Sawada and Yoshihiro   Hayakawa