A modified levenberg marquardt algorithm for simultaneous learning of multiple datasets.
Levenberg-Marquardt (LM) algorithm is a powerful approach to optimize the parameters of a neural network (NN). Given a training dataset, the algorithm synthesizes the best path toward the optimum. This brief demonstrates the use of LM optimization algorithm when there are more than one dataset and o...
| Main Authors: | , , , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers Inc.
2024
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| Summary: | Levenberg-Marquardt (LM) algorithm is a powerful approach to optimize the parameters of a neural network (NN). Given a training dataset, the algorithm synthesizes the best path toward the optimum. This brief demonstrates the use of LM optimization algorithm when there are more than one dataset and on/off type switching of NN parameters is allowed. For each dataset a pre-selected set of parameters are allowed for modification and the proposed scheme reformulates the Jacobian under the switching mechanism. The results show that a NN can store information available in different datasets by a simple modification to the original LM algorithm, which is the novelty introduced in this brief. The results are verified on a regression problem. |
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