Input: training set $Z=\left({x}_{k},{y}_{k}\right)$ ; learning rate |

Algorithm process: BEGIN: 1. Randomly initializing weights and thresholds within the range of (0, 1); 2. Repeat; 3. For all $\left({x}_{k},{y}_{k}\right)\in \text{Z}$ do 4. Calculating the output of the current sample from the current parameters and the training set; 5. Calculating a gradient term of the output layer neuron from the mean square error and the output of the current sample; 6. Calculating the gradient term of the hidden layer neuron according to the error; 7. Update weights and thresholds; 8. End for 9. Until reaches the stop condition; |

Output: charging station planning model, weighted and threshold-determined multilayer feedforward neural network |