In this paper a nonparametric neural network (NN) technique for prediction of future values of a signal based on its past history is presented. This approach bypasses modeling, identification, and parameter estimation phases that are required by conventional parametric techniques. A multi-layer feed forward NN is employed. It develops an internal model of the signal through a training operation...

One of the most pressing challenges prevalent in steel manufacturing industry is identification surface defects. Early casting defects can help boost performance, including streamlining production processes. Though, deep learning models have helped bridge this gap and automate these processes, there a dire need to come up with lightweight that be deployed easily faster inference times. This res...

Continuous phase modulation (CPM) schemes are generally used in peak-power limited transmission systems such as digital satellite communications. Their major drawback, however, is a prohibitively complex receiver structure, particularly in modulation schemes with high packing densities. In this paper we propose feed-forward neural nets (NN) as receivers for partial response CPM systems. Our app...

A functional differential equation that arises from the classic theory of neural networks is considered. As the length of the absolute refractory period is varied, there is, as shown here, a super-critical Hopf bifurcation. As the ratio of the refractory period to the time constant of the network increases, a novel relaxation oscillation occurs. Some approximations are made and the period of th...

We consider Whittle's probabilistic content addressable memory, the antiphon, which is designed for recovering stored patterns from nonlinear distortions of the input messages. We give an application to content-based image retrieval systems and propose canonical ways of choosing similarity thresholds ensuring statistical consistency.

Pitch detection based on neural-net classifiers is investigated. T o this end, the extent of generalization attainable with neural nets is first examined, and i t is shown t h a t a suitable choice of features is required t o utilize this property. Specifically, invaria n t features should be used whenever possible. For pitch detection, two feature sets, one based on waveform samples and the ot...

This paper discusses the application of neural network pattern analysis algorithms to the 1C fault diagnosis problem. A fault diagnostic is a decision rule combining what is known about an ideal circuit test response with information about how it is distorted by fabrication variations and measurement noise. The rule is used to detect fault existence in fabricated circuits using real test equipm...