Convergence of back-propagation in neural networks using a log-likelihood cost function

A variation of the back-propagation algorithm is described, using a log-likelihood cost function. Appropriate choices of learning parameters are discussed. An example is given where the range of initial weights leading to proper convergence is increased, and the number of iterations required is significantly reduced.