Abstract：Combined with the SPSS of the maximum rotation factor analysis of variance, a random harmonic algorithm was constructed for few data to rich, which improved double input power-activation feed-forward neural network.The algorithm effectively solved the power-activation feed-forward neural network in sampling data under less problem of low prediction accuracy.The optimal structure was determined based on the directweight-determination and optimal structure of double input power-activation improved feed-forward neural network.Then used the random harmonic algorithm to generate a large number of training data in limited sampling data, and determined the optimal weights of final network.Finally, the forecast verification data value in the given number of cycles was received.The results of numerical simulation showed that the algorithm has higher prediction accuracy.

Keyword：power-activation feed-forward neural network;random harmonic algorithm;gas emission quantity;