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Frequency division multiplexing in analogue neural network

Hayes-Gill, Barrie; Craven, Michael


Professor of Electronic Systems and Medical Devices


Frequency division multiplexing has been studied as a means of communication between neural layers in an analogue multilayered perceptron neural network architecture, trained using the back-propagation learning algorithm. Simulation results on network learning and generalisation show that the neural network is tolerant to as much as 50% overlap of frequency responses of filters used in demultiplexing. Thus, the number of communication channels available is considerably increased.

Journal Article Type Article
Publication Date May 23, 1991
Journal Electronics Letters
Print ISSN 0013-5194
Publisher Institution of Engineering and Technology
Peer Reviewed Peer Reviewed
Volume 27
Issue 11
Pages 918-920
APA6 Citation Hayes-Gill, B., & Craven, M. (1991). Frequency division multiplexing in analogue neural network. Electronics Letters, 27(11), 918-920. doi:10.1049/el:19910575
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