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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.

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