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Improved Computation for Levenberg–Marquardt Training

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2 Author(s)
Wilamowski, B.M. ; Dept. of Electr. & Comput. Eng., Auburn Univ., Auburn, AL, USA ; Hao Yu

The improved computation presented in this paper is aimed to optimize the neural networks learning process using Levenberg-Marquardt (LM) algorithm. Quasi-Hessian matrix and gradient vector are computed directly, without Jacobian matrix multiplication and storage. The memory limitation problem for LM training is solved. Considering the symmetry of quasi-Hessian matrix, only elements in its upper/lower triangular array need to be calculated. Therefore, training speed is improved significantly, not only because of the smaller array stored in memory, but also the reduced operations in quasi-Hessian matrix calculation. The improved memory and time efficiencies are especially true for large sized patterns training.

Published in:

Neural Networks, IEEE Transactions on  (Volume:21 ,  Issue: 6 )