Accession Number : ADA259081


Title :   Signal Approximation with a Wavelet Neural Network


Descriptive Note : Master's thesis


Corporate Author : AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF ENGINEERING


Personal Author(s) : Westphal, Charles M


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a259081.pdf


Report Date : Dec 1992


Pagination or Media Count : 57


Abstract : This study investigated the use of Wavelet Neural Networks (WNN) for signal approximation. The particular wavelet function used in this analysis consisted of a summation of sigmoidal functions (a sigmoidal wavelet). The sigmoidal wavelet has the advantage of being easily implemented in hardware via specialized electronic devices like the Intel Electronically Trainable Analog Neural Network (ETANN) chip. The WNN representation allows the determination of the number of hidden-layer nodes required to achieve a desired level of approximation accuracy. Results show that a bandlimited signal can be accurately approximated with a WNN trained with irregularly sampled data. Signal approximation, Wavelet neural network.


Descriptors :   *SIGNAL PROCESSING , *NEURAL NETS , FUNCTIONS , NODES , ANALOGS , NUMBERS , SIGNALS , ACCURACY , ELECTRONICS


Subject Categories : Computer Systems


Distribution Statement : APPROVED FOR PUBLIC RELEASE