In this study we introduce a new approach to train a fully recurrent artificial neural network by solving a constraint satisfaction problem using the quotient gradient method. The quotient gradient method is a trajectory based methodology for global optimization that does not suffer from the problem of local minima encountered in Newton based methods. Simulation results show that the network trained with the quotient gradient method perform better than traditional error backpropagation. The method is also easier to implement in comparison to other global optimization techniques such as genetic algorithms.
Adaptive natural gradient learning avoids singularities in the parameter space of multilayer perceptrons. However, it requires a larger number of additional parameters than ordinary backpropagation in the form of the Fisher information matrix. This article describes a new approach to natural gradient learning that uses a smaller Fisher information matrix. It also uses a prior distribution on the neural network parameters and an annealed learning rate. While this new approach is computationally simpler, its performance is comparable to that of Adaptive natural gradient learning.
The oxidative polymorphism of debrisoquine (DBQ) has been determined in 89 patients with colo-rectal cancer and in 556 normal control subjects. Four patients and 34 controls, with a metabolic ratio >12.6, were classified as poor metabolisers of DBQ (n.s.).