Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
381684 | Engineering Applications of Artificial Intelligence | 2006 | 7 Pages |
Abstract
The Levenberg–Marquardt algorithm is considered as the most effective one for training artificial neural networks but its computational complexity and the difficulty to compute the trust region have made it very difficult to develop a true iterative version to use in on-line training. The algorithm is frequently used for off-line training in batch versions although some attempts have been made to implement iterative versions. To overcome the difficulties in implementing the iterative version, a batch-sliding window with Early Stopping, which uses a hybrid Direct/Specialized evaluation procedure, is proposed and tested with a real system.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Fernando Morgado Dias, Ana Antunes, José Vieira, Alexandre Mota,