Prediction of length sequence of railways in operation based on genetic algorithm and simulated annealing algorithm optimized neural networks

A method is proposed for the prediction of length sequence of railways in operation based on genetic algorithm (GA) and simulated annealing (SA) optimized neural networks. A three-layer feedforward neural network (5 input neurons, 8 hidden neurons, 1 output neuron) is applied to the length sequence prediction of railways in operation. To obtain optimal weights, GA and SA algorithms are integrated to train the neural network. To combine these two algorithms, GA is put into each step of the SA algorithm. The weights that are all real numbers are coded as chromosomes of the GA. Compared with that of BP neural network, the numerical results show that this model has the advantages of high prediction accuracy and high operational speed, the method is feasible.