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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.


Hayes-Gill, B., & Craven, M. (1991). Frequency division multiplexing in analogue neural network. Electronics Letters, 27(11), 918-920. doi:10.1049/el:19910575

Journal Article Type Article
Publication Date May 23, 1991
Deposit Date Nov 27, 2018
Journal Electronics Letters
Print ISSN 0013-5194
Publisher Institution of Engineering and Technology
Peer Reviewed Peer Reviewed
Volume 27
Issue 11
Pages 918-920
Public URL
Publisher URL