Multistage neural network structure for transient detection and feature extraction

A system of neural networks in a multistage architecture is proposed to resolve the components of a transient signal. The motivation of the design was a desire to use smaller networks that compute a binary decision allowing the use of the multilayer perceptron design algorithm for faster and more effective training, and to cascade these simple networks into a pipeline architecture for efficient implementation. Simulation results are compared with the multistage subspace technique that utilizes all of the information in the signal model. The networks are trained with examples of one signal component in noise at a specified noise level. The resulting multistage neural network is able to generalize to different noise levels and multiple signals without additional training. The neural network detector and feature extraction system localizes the arrival time and frequency for each sufficiently strong transient signal that is present.<>