A new training algorithm for the general regression neural network
Timothy D. Masters, Walter Gottlieb Land · 2002
The general regression neural network (GRNN) is known to be widely effective for modeling and prediction, especially if separate sigma weights are used for each predictor. However, the significant time requirements for executing the model, combined with the frequent presence of multiple local optima, makes it difficult to train this model in many applications. This paper shows how differential evolution may be enhanced by direct gradient descent to produce a hybrid training algorithm that is both fast and effective.