Title :
Learnings and applications of feedforward nets
Author_Institution :
Dept. of Maths., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
A three-layer network which approximates a desired function f by a piecewise constant function is constructed. Backpropagation and classic gradient learning are present. A learning method is presented which gives the optimal weights at each iteration. Applications to pattern recognition are given with discussions on using RBF (radical basis function) unit networks. In addition, it is proved that the error is bounded by a linear function of the grid size
Keywords :
backpropagation; feedforward neural nets; learning (artificial intelligence); pattern recognition; backpropagation; classic gradient learning; feedforward nets; linear function; optimal weights; pattern recognition; piecewise constant function; radical basis function; three-layer network; Fourier series; Interpolation; Learning systems; Mathematical model; Mathematics; Neurons; Pattern recognition; Polynomials; Supervised learning;
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
DOI :
10.1109/IJCNN.1992.287110
