Hessian with Mini-Batches for Electrical Demand Prediction
2020
Israel Elias | José de Jesús Rubio | David Ricardo Cruz | Genaro Ochoa | Juan Francisco Novoa | Dany Ivan Martinez | Samantha Muñiz | Ricardo Balcazar | Enrique Garcia | Cesar Felipe Juarez
The steepest descent method is frequently used for neural network tuning. Mini-batches are commonly used to get better tuning of the steepest descent in the neural network. Nevertheless, steepest descent with mini-batches could be delayed in reaching a minimum. The Hessian could be quicker than the steepest descent in reaching a minimum, and it is easier to achieve this goal by using the Hessian with mini-batches. In this article, the Hessian is combined with mini-batches for neural network tuning. The discussed algorithm is applied for electrical demand prediction.
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