On Speeding up the Levenberg-Marquardt Learning Algorithm.

A new approach to the practical realizations of calculations to the Levenberg-Marquardt learning algorithm is presented. The proposed solutions aim to effectively reduce the high computational load of the LM algorithm. The detailed application of proposed methods in the process of learning neural networks is explicitly discussed. Experimental results have been obtained for all proposed methods and they confirm a very good performance of them.