A 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 nonstationary (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.