DocumentCode :
1402824
Title :
Introduction to the modified probabilistic neural network for general signal processing applications
Author :
Zaknich, Anthony
Author_Institution :
Dept. of Electr. & Electron. Eng., Western Australia Univ., Nedlands, WA, Australia
Volume :
46
Issue :
7
fYear :
1998
fDate :
7/1/1998 12:00:00 AM
Firstpage :
1980
Lastpage :
1990
Abstract :
This paper introduces a practical and easy-to-understand network for signal processing called the modified probabilistic neural network (MPNN). It begins with a short introduction to the application of artificial neural networks to signal processing followed by a background and review of the MPNN theory. The MPNN is a regression technique similar to Specht´s (1991) general regression neural network, which is based on a single radial basis function kernel whose bandwidth is related to the noise statistics. It has advantages in application to time and spatial series signal processing problems because it is constructed directly and simply from the training signal waveform characteristics or features. An illustrative example involving noisy Doppler-shifted swept frequency sonar signal detection compares the effectiveness of the first- and second-order Volterra, multilayer perceptron neural network, radial basis function neural network, general regression neural network and MPNN filters, demonstrating some features of the MPNN for practical design
Keywords :
Doppler shift; acoustic signal detection; feedforward neural nets; signal processing; sonar signal processing; statistical analysis; MPNN; MPNN filters; artificial neural networks; first-order Volterra neural network; general regression neural network; general signal processing applications; modified probabilistic neural network; multilayer perceptron neural network; noise statistics; noisy Doppler-shifted swept frequency sonar signal detection; radial basis function kernel; radial basis function neural network; regression technique; second-order Volterra neural network; spatial series signal processing; time series signal processing; training signal waveform characteristics; Artificial neural networks; Bandwidth; Frequency; Kernel; Multi-layer neural network; Neural networks; Signal detection; Signal processing; Sonar detection; Statistics;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/78.700969
Filename :
700969
Link To Document :
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