doi:10.1093/comjnl/bxp101 Levenberg—Marquardt Training Algorithms for Random Neural Networks

Random neural networks (RNN) have been efficiently used as learning tools in many applications of different types. The learning procedure followed so far is the gradient descent one. In this paper we explore the use of the Levenberg—Marquardt (LM) optimization procedure, more powerful when it is applicable, together with one of its major extensions, the LM procedure with adaptive momentum. We show how these methods can be used with RNN and run several experiments to evaluate their performances. The use of these techniques in the case of RNN lead to similar conclusions than when using standard artificial neural network: they clearly improve the learning efficiency