Temporal pattern recognition using one-memory-element-per-state sequential neural network

Tet H. Yeap; S. G. Zaky; John K. Tsotsos; H. C. Kwan

doi:10.1117/12.152649

19 August 1993 Temporal pattern recognition using one-memory-element-per-state sequential neural network

Tet H. Yeap, S. G. Zaky, John K. Tsotsos, H. C. Kwan

Proceedings Volume 1966, Science of Artificial Neural Networks II; (1993) https://doi.org/10.1117/12.152649
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

This paper presents a special sequential neural network for temporal pattern recognition using one memory element per state. The network generates a single response representing a sequence of events by utilizing the process of temporal integration. That is, the response is generated in small increments at each time step by summing in time the recognition result of each event. Models for motion detection and speech recognition based on the proposed network were implemented. Simulation results show that the network is tolerant to noise, and can recognize partial sequences.

Citation Download Citation

Tet H. Yeap, S. G. Zaky, John K. Tsotsos, and H. C. Kwan "Temporal pattern recognition using one-memory-element-per-state sequential neural network", Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); https://doi.org/10.1117/12.152649

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