Now showing items 1-4 of 4

    • Comparison of function approximation with sigmoid and radial basis function networks 

      Russel, Gary; Fausett, Laurene V (1996-03-22)
      Theoretical and computational results have demonstrated that several types of neural networks have the universal approximation property, i.e., the ability to represent any continuous function to an arbitrary degree of ...
    • Comparison of three clustering algorithms and an application to color image compression 

      Cha, Jihun; Fausett, Laurene V (1997-04-04)
      This paper investigates a traditional clustering algorithm (K-means) and two neural networks (SOM and ART-F). The characteristics of each algorithm are illustrated by simulating geometric space data clustering. Then each ...
    • Function approximation using a sinc neural network 

      Elwasif, Wael R.; Fausett, Laurene V (1996-03-22)
      Neural networks for function approximation are the basis of many applications. Such networks often use a sigmoidal activation function (e.g. tanh) or a radial basis function (e.g. gaussian). Networks have also been developed ...
    • Similarity-based learning for pattern classification 

      Fausett, Laurene V (1996-03-22)
      Several standard neural networks, including counterpropagation networks, predictive ART networks, and radial basis function networks, are based on a combination of clustering (unsupervised learning) and mapping (supervised ...