Introduction to the Modified Probabilistic Neural Network for General Signal Processing Applications

Anthony Zaknich

    Research output: Contribution to journalArticle

    70 Citations (Scopus)

    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 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.
    Original languageEnglish
    Pages (from-to)1980-1990
    JournalIEEE Transactions on Signal Processing
    Volume46
    Issue number7
    DOIs
    Publication statusPublished - 1998

      Fingerprint

    Cite this