Neural Network Hybrid Learning: Genetic Algorithms & Levenberg-Marquardt
نویسندگان
چکیده
The success of an Artificial Neural Network (ANN) strongly depends on its training process. Gradient-based techniques have been satisfactorily used in the ANN training. However, in many cases, these algorithms are very slow and susceptible to the local minimum problem. In our work, we implemented a hybrid learning algorithm that integrates Genetic Algorithms(GAs) and the LevenbergMarquardt(LM) algorithm, a second order gradient-based technique. The GA-LM algorithm was used to train a Time-Delay Neural Network for river flow prediction. In our experiments, the GA-LM hybrid algorithm obtained low prediction errors within a short execution time.
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