Abstract: During signal transmission, the combined effect of the
transmitter filter, the transmission medium, and additive white
Gaussian noise (AWGN) are included in the channel which distort
and add noise to the signal. This causes the well defined signal
constellation to spread causing errors in bit detection. A compact pi
neural network with minimum number of nodes is proposed. The
replacement of summation at each node by multiplication results in
more powerful mapping. The resultant pi network is tested on six
different channels.
Abstract: The back propagation algorithm calculates the weight
changes of artificial neural networks, and a common approach is to
use a training algorithm consisting of a learning rate and a
momentum factor. The major drawbacks of above learning algorithm
are the problems of local minima and slow convergence speeds. The
addition of an extra term, called a proportional factor reduces the
convergence of the back propagation algorithm. We have applied the
three term back propagation to multiplicative neural network
learning. The algorithm is tested on XOR and parity problem and
compared with the standard back propagation training algorithm.