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Proceedings Paper

Modified backpropagation neural network with applications to image compression
Author(s): Surender K. Kenue
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Paper Abstract

The back-propagation neural network algorithm is an iterative technique for learning the relationship between an input and output. This algorithm has been successfully used in many real-world applications; however, it suffers from slow convergence problems and can get struck in local minima of the weight-error surface. A generalization of previously proposed activation functions has been developed using a free parameter for improved convergence. A modified algorithm based on these function is suggested by bounding the input data to the derivative evaluation in the backwards pass. The modified algorithm has demonstrated superior performance on the standard parity and encoder problems. Finally, a histogram normalization technique is presented for image data compression for improved convergence.

Paper Details

Date Published: 16 September 1992
PDF: 14 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140017
Show Author Affiliations
Surender K. Kenue, General Motors Research Labs. (United States)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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