A comprehensive study for real-time learning of wave-net models of a nonlinear time-varying experimental process

This paper presents a real time on-line learning study of an experimental thermal process based on on-line wave-nets. Wave-nets are wavelet based neural networks with localized and hierarchical multi-resolution learning. The multi-resolution framework of wave-nets allows nonlinear modeling of any complicated systems. The recently developed on-line features to wave-net learning have enhanced their capability in learning and adaptation of any non-linear time varying systems (with low dimensions). A real experimental time varying thermal process is modeled and simulated to show the applicability of these algorithms. The platform used for the Real-Time implementation is MATLAB/REAL TIME TOOLBOX using a DAQ interface. The results show the effectiveness of the methods.