Improved signal processing with dynamic recurrent neural models using ARMA-like units

We have shown that dynamic recurrent neural networks with ARMA-like units can tackle the problem of complex signal processing. In some cases of very highly nonlinear processing, their use can even be inevitable. We have shown that the Pontryagin Maximum Principle (from the theory of control) helps to elegantly derive the continuous-time learning algorithms for these complex neural architectures Finally, we have presented practical biomedical application where dynamic recurrent networks exhibit, their robustness. We are currently investigating other applications in the field of mathematics (such as interpolation tasks i.e., for the forecasting of stock market value) and of engineer