New neural network structure for temporal signal processing

In this paper a new two-layer linear-in-the-parameters feedforward network termed the Functionally Expanded Neural Network (FENN) is presented, together with its design strategy and learning algorithm. It is essentially a hybrid neural network incorporating a variety of non-linear basis functions within its single hidden layer which emulate other universal approximators employed in the conventional Multi-Layered Perceptron (MLP), Radial Basis Function (RBF) and Volterra Neural Networks (VNN). The FENN's output error surface is shown to be uni-modal allowing high speed single run learning. A simple strategy based on an iterative pruning retraining scheme coupled with statistical model validation tests is proposed for pruning the FENN. Both simulated chaotic (Mackey Glass time series) and real-world noisy, highly non-stationary (sunspot) time series are used to illustrate the superior modeling and prediction performance of the FENN compared with other recently reported, more complex neural network based predictor models.