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Convergent newton method and neural network for the electric energy usage prediction
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NSTL
Elsevier
In the neural network adaptation, the Newton method could find a minimum with its second-order partial derivatives, and convergent gradient steepest descent could assure its error convergence with its time-varying adaptation rates. In this article, the convergent Newton method is proposed as the combination of the Newton method and the convergent gradient steepest descent for the neural networks adaptation, where the convergent Newton method incorporates the second-order partial derivatives inside of the time-varying adaptation rates. Hence, the convergent Newton method could assure its error con-vergence and could find a minimum. Experiments show that the convergent Newton method obtains satisfactory results in the electric energy usage data prediction. (c) 2021 Elsevier Inc. All rights reserved.
PredictionAdaptationNewton methodGradient steepest descentError convergenceElectric energy usageOPTIMIZATION
de Jesus Rubio, Jose、Antonio Islas, Marco、Ochoa, Genaro、Ricardo Cruz, David、Garcia, Enrique、Pacheco, Jaime
NSTL
Elsevier
