The chaotic time series forecast using wavelet neural networks WNN) was researched in this paper. An improved training method for WNN was presented. This method combines the genetic arithmetic (GA) and gradient descent BP method, and the BP method was embedded in the GA operation in order to resolve the GA's limitation in detail search capability. In the last step of this method the WNN trained by GA searches the best solution using BP method once again. The experiment on predicting the chaotic time series from Henon map validates the performance of the method in this paper; the experimental result also shows the method could assure the WNN convergence and have high forecasting precision.
Jassem, W. (1989). Speech Synthesis and Recognition. Journal of Phonetics, 17(3), 245–247. https://doi.org/10.1016/s0095-4470(19)30433-4