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Training feedforward networks with the Marquardt algorithm

Martin Hagan; Mohammad Bagher Menhaj · IEEE Transactions on Neural Networks · 1994

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Author / responsible party
Martin Hagan; Mohammad Bagher Menhaj
Publisher
IEEE Transactions on Neural Networks
Year
1994
Language
English

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The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks. The algorithm is tested on several function approximation problems, and is compared with a conjugate gradient algorithm and a variable learning rate algorithm. It is found that the Marquardt algorithm is much more efficient than either of the other techniques when the network contains no more than a few hundred weights.

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APA 7

Hagan, M. & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. https://doi.org/10.1109/72.329697

MLA

Hagan, Martin, and Mohammad Bagher Menhaj. "Training feedforward networks with the Marquardt algorithm." 1994. https://doi.org/10.1109/72.329697.

Chicago

Hagan, Martin and Mohammad Bagher Menhaj. 1994. "Training feedforward networks with the Marquardt algorithm.". https://doi.org/10.1109/72.329697.

Harvard

Hagan, M. and Menhaj, M. B. 1994, Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks, available at: https://doi.org/10.1109/72.329697 [Accessed 7 Aug. 2026].

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Title
Training feedforward networks with the Marquardt algorithm
Author / contributors
Martin Hagan; Mohammad Bagher Menhaj
Publisher
IEEE Transactions on Neural Networks
Publication year
1994
Language
English

Subjects

Explore related resources through these subjects.

Backpropagation; Algorithm; Levenberg–Marquardt algorithm; Artificial neural network; Rprop; Conjugate gradient method; Computer science; Feedforward neural network; Artificial intelligence; Function (biology); Feed forward; Nonlinear system
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