Time Recurrent Learning Algorithm Research Articles

NOVEL ADAPTIVE FILTERING TECHNIQUE FOR THE PROCESSING OF ELECTROGASTROGRAM

The electrogastrogram measured cutaneously by attaching electrodes to the abdominal skin contains considerable noise. An attempt is made to develop an efficient adaptive filtering technique suitable for real time processing of electrogastrogram. A novel technique combining both adaptive noise cancellation and adaptive signal enhancement in a single recurrent neural network is proposed. To compare its performance, adaptive noise cancellation and cascaded connection of adaptive noise cancellation and adaptive signal enhancement are employed. Recurrent neural networks using Real Time Recurrent Learning (RTRL) algorithm are employed for implementing the all the above systems. An attempt is made to alleviate the computational burden imposed by RTRL algorithm by employing pruning of weights.

ON MODIFIED COMPLEX RECURRENT NEURAL NETWORK ADAPTIVE EQUALIZER

A new modified version called decision feedback complex recurrent neural network equalizer (DFCRNNE) is proposed with the study of complex recurrent neural network equalizer (CRNNE). Based on DFCRNNE, a modified real time recurrent learning (CRTRL) algorithm is developed. Simulation results show that DFCRNNE has better performance than CRNNE based on traditional CRTRL algorithm2 in complex nonlinear channels with severe intersymbol interference (ISI) and nonlinear distortion.