LEARNING ALGORITHMS FOR NEURAL NETWORKS BASED ON QUASI-NEWTON METHODS WITH SELF-SCALING

Previous studies have suggested that, for moderate sized neural networks, the use of classical Quasi-Newton methods yields the best convergence properties among all the state-of-the-art [1]. This paper describes a set of even better learning algorithms based on a class of Quasi-Newton optimization techniques called Self-Scaling Variable Metric (SSVM) methods. One of the characteristics of SSVM methods is that they provide a set of search directions which are invariant under the scaling of the objective function. With an XOR benchmark and an encoder benchmark, simulations using the SS VM algorithms for the learning of general feedforward neural networks were carried out to study their performance. Compared to classical Quasi-Newton methods, it is shown that the SSVM method reduces the number of iterations required for convergence by 40 percent to 60 percent that of the classical Quasi-Newton methods which, in general, converge two to three orders of magnitude faster than the steepest descent techniques.