Convergent newton method and neural network for the electric energy usage prediction

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 convergence and could find a minimum. Experiments show that the convergent Newton method obtains satisfactory results in the electric energy usage data prediction.