A self-organizing neural network for classifying sequences
- 1 January 1989
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 561-568 vol.2
- https://doi.org/10.1109/ijcnn.1989.118299
Abstract
The ability to recognize sequences is important for applications such as speech processing, vision, and control systems. A self-organizing neural network model that is able to form an ordered map of a sequence is presented. The model is based on extensions to T. Kohonen's self-organizing topology maps (Self-Organization and Associative Memory, Springer-Verlag, 1984). Theoretical results and simulations are presented that demonstrate the ability of the model to learn arbitrary sequences of n-dimensional patterns. The network model represents a learned sequence with a fixed sequence of network outputs that is easily identifiable. This representation makes the development of a sequence classifier relatively simple.Keywords
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