The Subjected SPDS Algorithm of Forward Neural Network

An Analog Neural Network for Wideband Predistortion of Pico-cell Power Amplifiers

“…We thus expect to see convergence times with an underlying proportionality to because as reduces, the probability of weight clipping increases leading to a lower determinant of the Hessian matrix approximation and consequently a higher rate of convergence. [24]. Reference [22] normalizes the network weights to a predetermined maximum value after each iteration, this leads to convergence issues based on the choice of maximum weight.…”

“…In [23], analog circuits are designed to yield a gain of 5 times the activation function range, a method which becomes invalid for a dynamic network with multiple neurons as gain variation increases with non-linearity and bandwidth. Finally, [24] optimizes the neural network one weight at a time which is an inefficient training method for large networks.…”