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Proceedings Paper

Spatiotemporal topology and temporal sequence identification with an adaptive time-delay neural network
Author(s): Daw-Tung Lin; Panos A. Ligomenides; Judith E. Dayhoff
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Paper Abstract

Inspired from the time delays that occur in neurobiological signal transmission, we describe an adaptive time delay neural network (ATNN) which is a powerful dynamic learning technique for spatiotemporal pattern transformation and temporal sequence identification. The dynamic properties of this network are formulated through the adaptation of time-delays and synapse weights, which are adjusted on-line based on gradient descent rules according to the evolution of observed inputs and outputs. We have applied the ATNN to examples that possess spatiotemporal complexity, with temporal sequences that are completed by the network. The ATNN is able to be applied to pattern completion. Simulation results show that the ATNN learns the topology of a circular and figure eight trajectories within 500 on-line training iterations, and reproduces the trajectory dynamically with very high accuracy. The ATNN was also trained to model the Fourier series expansion of the sum of different odd harmonics. The resulting network provides more flexibility and efficiency than the TDNN and allows the network to seek optimal values for time-delays as well as optimal synapse weights.

Paper Details

Date Published: 20 August 1993
PDF: 10 pages
Proc. SPIE 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques, (20 August 1993); doi: 10.1117/12.150167
Show Author Affiliations
Daw-Tung Lin, Univ. of Maryland/College Park (United States)
Panos A. Ligomenides, Univ. of Maryland/College Park (United States)
Judith E. Dayhoff, Univ. of Maryland/College Park (United States)

Published in SPIE Proceedings Vol. 2055:
Intelligent Robots and Computer Vision XII: Algorithms and Techniques
David P. Casasent, Editor(s)

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