Training autoassociative recurrent neural network with preprocessed training data

Arun Maskara; Andrew Noetzel

doi:10.1117/12.152645

19 August 1993 Training autoassociative recurrent neural network with preprocessed training data

Arun Maskara, Andrew Noetzel

Proceedings Volume 1966, Science of Artificial Neural Networks II; (1993) https://doi.org/10.1117/12.152645
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

The Auto-Associative Recurrent Network (AARN), a modified version of the Simple Recurrent Network (SRN) can be trained to behave as recognizer of a language generated by a regular grammar. The network is trained successfully on an unbounded number of sequences of the language, generated randomly from the Finite State Automation (FSA) of the language. But the training algorithm fails when training is restricted to a fixed finite set of examples. Here, we present a new algorithm for training the AARN from a finite set of language examples. A tree is constructed by preprocessing the training data. The AARN is trained with sequences generated randomly from the tree. The results of the simulations experiments are discussed.

Citation Download Citation

Arun Maskara and Andrew Noetzel "Training autoassociative recurrent neural network with preprocessed training data", Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); https://doi.org/10.1117/12.152645

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