The implementation of Rprop has been changed in two ways: First, the implementation now follows a slightly modified adaptation scheme. Essentially, the backtracking step is no longer performed, if a jump over a minimum occurred. Second, a weight-decay term is introduced. The weight-decay parameter (the third learning parameter) determines the relationship of two goals, namely to reduce the output error (the standard goal) and to reduce the size of the weights (to improve generalization). The composite error function is:
Important: Please note that the weight decay parameter denotes the exponent, to allow comfortable input of very small weight-decay. A choice of the third learning parameter corresponds to a ratio of weight decay term to output error of .