Computationally Efficient locally-Recurrent Neural Networks for on-line signal processing

A general class of Computationally Efficient locally Recurrent Networks (CERN) is described for real-time adaptive signal processing. The structure of the CERN is based on linear-in-the-parameters single-hidden-layered feedforward neural networks such as the Radial Basis Function (RBF) network, the Volterra Neural Network (VNN) and the recently developed Functionally Expanded Neural Network (FENN), adapted to employ local output feedback. The corresponding learning algorithms are derived and key structural and computational complexity comparisons are made between the CERN and conventional Recurrent Neural Networks. Two case studies are performed involving the real-time adaptive non-linear prediction of real-world chaotic, highly non-stationary laser time series and an actual speech signal, which show that a Recurrent FENN based adaptive CERN predictor can significantly outperform the corresponding feedforward FENN and conventionally employed linear adaptive filtering models.