Abstract
A novel learning algorithm is developed for the training of multilayer feedforward neural networks, based on a modification of the Marquardt-Levenberg least-squares optimization method. The algorithm updates the input weights of each neuron in the network in an effective parallel way. An adaptive distributed selection of the convergence rate parameter is presented, using suitable optimization strategies. The algorithm has better
Keywords
BackpropagationArtificial neural networkComputer scienceConvergence (economics)AlgorithmRpropRate of convergenceLevenberg–Marquardt algorithmFeedforward neural networkArtificial intelligenceFeed forwardTypes of artificial neural networksTime delay neural networkKey (lock)Engineering
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Publication Info
- Year
- 1989
- Type
- article
- Volume
- 36
- Issue
- 8
- Pages
- 1092-1101
- Citations
- 198
- Access
- Closed
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Cite This
Stefanos Kollias,
Dimitris Anastassiou
(1989).
An adaptive least squares algorithm for the efficient training of artificial neural networks.
IEEE Transactions on Circuits and Systems
, 36
(8)
, 1092-1101.
https://doi.org/10.1109/31.192419
Identifiers
- DOI
- 10.1109/31.192419