Stable Dynamic Parameter Adaption

A stability criterion for dynamic parameter adaptation is given. In the case of the learning rate of backpropagation, a class of stable algorithms is presented and studied, including a convergence proof. 1 INTRODUCTION All but a few learning algorithms employ one or more parameters that control the quality of learning. Backpropagation has its learning rate and momentum parameter; Boltzmannlearning uses a simulated annealing schedule; Kohonen learning a learning rate and a decay parameter; genetic algorithms probabilities, etc. The investigator always has to set the parameters to specific values when trying to solve a certain problem. Traditionally, the metaproblem of adjusting the parameters is solved by relying on a set of well-tested values of other problems or an intensive search for good parameter regions by restarting the experiment with different values. Inthis situation, a great deal of expertise and/or time for experiment design is required (as well as a huge amount of computing time).