NDLI: Temporal backpropagation for FIR neural networks

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International 1989 Joint Conference on Neural Networks

Temporal backpropagation for FIR neural networks

Content Provider	IEEE Xplore Digital Library
Author	Wan, E.A.
Copyright Year	1990
Abstract	The traditional feedforward neural network is a static structure which simply maps input to output. To better reflect the dynamics in a biological system, a network structure which models each synapse by a finite-impulse response (FIR) linear filter is proposed. An efficient-gradient descent algorithm which is shown to be a temporal generalization of the familiar backpropagation algorithm is derived. By modeling each synapse as a linear filter, the neural network as a whole may be thought of as an adaptive system with its own internal dynamics. Equivalently, one may think of the network as a complex nonlinear filter. Applications should thus include areas of pattern recognition where there is an inherent temporal quality to the data, such as in speech recognition. The networks should also find a natural use in areas of nonlinear control, and other adaptive signal processing and filtering applications such as noise cancellation or equalization
Starting Page	575
Ending Page	580
File Size	363228
Page Count	6
File Format	PDF
DOI	10.1109/IJCNN.1990.137629
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1990-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article

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