Optimum Finite Memory Nonlinear Filter

Optimum Finite Memory Nonlinear Filter

"This thesis investigates the possibility of using the Zadah theory and a machine type computer to design an optimum finite memory nonlinear filter for extraction of Poisson distributed signal pulses from a background of Gaussian noise. Basically the procedure involves the technique of solving simultaneous equations by the computer. We demonstrate that it is possible to find the limitation of the filter of class N1 in terms of an absolute minimum mean square error. However, due to the capacity of the computer we do not actually determine the absolute minimum. Nevertheless we do show that the mean square error decreases with increasing memory time T"--Abstract, leaf ii.

Journal of Research of the National Bureau of Standards

Handbook of Neural Network Signal Processing

The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together applications that were previously scattered among various publications to provide an up-to-date, detailed treatment of the subject from an engineering point of view. The authors cover basic principles, modeling, algorithms, architectures, implementation procedures, and well-designed simulation examples of audio, video, speech, communication, geophysical, sonar, radar, medical, and many other signals. The subject of neural networks and their application to signal processing is constantly improving. You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field.