Balance optimization method of energy shipping based on Hopfield neural network

It is of great significance for the optimization of transportation strategy to study the methods of energy shipping scheduling. Based on the Hopfield neural network (HNN) theory, this paper proposes Hopfield neural network energy transportation path optimization algorithm with improved activation function, which solves the problems of poor mapping ability, low flexibility and high sensitivity of neurons near zero to input of traditional activation function. The improved activation function can reduce the derivative value of the activation function and the sensitivity of neurons near zero to the input value by flexibly adjusting the steepness, positioning and mapping range of energy sea transportation at the same time. The experimental results show that: under the same initial conditions, the model proposed in this paper shows better error code performance, and the data length of the required transmission sequence is shorter. When sending 8PSK (Phase Shift Keying) signal with a data length of N = 40, the error rate of the 2-path synthesized random channel is less than 0.112. The error code performance and convergence speed of the energy ship-ping path planning algorithm are improved effectively.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).