A neural network learning process provides a trained network that has good generalization ability for even highly nonlinear dynamic systems, and is trained with approximations of a signal obtained, each at a different respective resolution, using wavelet transformation. Approximations are used in order from low to high. The trained neural network is used to predict values. In a preferred embodiment of the invention, the trained neural network is used in predicting network traffic patterns.

 
Web www.patentalert.com

< Systems and methods for generating string correlithm objects

< Goal based educational system with support for dynamic characteristic tuning

> IC for universal computing with near zero programming complexity

> Advanced recipe—a knowledge based information system for production processes

~ 00208