Frequency division multiplexing in analogue neural network
Michael P. Craven, K.M. Curtis, Barrie Hayes‐Gill · Electronics Letters · 1991
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.