Learning of dynamic variations of N-dimension patterns in a noniterative neural network

Chia-Lun John Hu; Sirikahlaya Chanekasit

doi:10.1117/12.602946

28 March 2005 Learning of dynamic variations of N-dimension patterns in a noniterative neural network

Chia-Lun John Hu, Sirikahlaya Chanekasit

Proceedings Volume 5816, Optical Pattern Recognition XVI; (2005) https://doi.org/10.1117/12.602946
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

In a set of preprocessed N-dimension, analog pattern vectors {U_m, m=1 to M, each U_m represents a distinct pattern}, if N>>M, then a one-layered sign-function neural network (OLNN) is sufficient to do a very robust, yet very accurate, noniterative-learning of all patterns. After the learning is done, the OLNN will make an accurate identification on an untrained test pattern even when the test pattern is varying within a certain dynamic range of a particular standard pattern learned during the noniterative learning process. The analytical foundation for making this dynamic neural network pattern recognition possible is the following. If we know that a standard pattern U_m will vary gradually among K boundary patterns U_m1 to U_mk, then we can train the neural network noniteratively to learn JUST THE BOUNDARY vectors {U_mi, i=1 to k} for each pattern U_m. Then, due to a distinctive property of noniterative learning, for a test input pattern U_t equal to any graduate changes within the boundaries (i.e., U_t = any CONVEX combination of the boundary set {U_mi, i=1 to k, m fixed.}), the OLNN can still automatically recognize this changed pattern even though all these gradually changed pattern are NOT learned step by step in the noniterative learning.

Citation Download Citation

Chia-Lun John Hu and Sirikahlaya Chanekasit "Learning of dynamic variations of N-dimension patterns in a noniterative neural network", Proc. SPIE 5816, Optical Pattern Recognition XVI, (28 March 2005); https://doi.org/10.1117/12.602946

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