A comparative study of fast neural network learning algorithms
In this paper, a comparative study of different neural network fast training procedures is presented. A decade of progress in speeding the feedforward neural network learning algorithms is reviewed. Different algorithms and techniques in unified form are presented and discussed with particular emphasis on their corresponding behaviour including the reduction of the number of iteration, their complexity, their accuracy and other parameters. Issues for the choice of faster algorithms are covered. An overview of further up to date new techniques is also discussed. Simulation results on a couple of benchmarks namely a 4-bit parity checker and the circle in the square problem are given to compare the proposed fast algorithms.