'2nd-degree' LSTMs with an LSTM for the state? • /r/MachineLearning

The wave equation was something like Asin(a1) Bsin(a2) C, with a1 and a2 both having different initial phases and frequencies. The LSTM alone couldn't figure out the wave (normalized), but when I fed in x, x' and x'' (first and second derivatives of the value), the model could understandably learn the pattern faster. Now, I have been trying a new technique where I have a'front' LSTM. Now at every iteration, this LSTM's total state (h:c) go through another'meta' LSTM (whose state is 2*state of the front LSTM), and then gets fed back into the front LSTM. The intuition for this, is that the meta-LSTM will provide a higher degree of abstraction/understanding over the trends, and front-LSTM will use the understanding of these predicted trends, to compute the future value.