The Improvement of sparrow search algorithm and the optimization of BP neural network for short term traffic flow prediction
The short-term traffic flow prediction by BP neural network is too dependent on the initial parameters.In order to solve this problem and optimize the BP neural network,a short-term traffic flow prediction model(ISSA-BP)is proposed based on the improved spar-row search algorithm(ISSA).Since the standard sparrow search algorithm(SSA)is easy to converge at the origin and fall into local optimum,the position update formulas of the dis-coverers and of some joiners in the sparrow group are improved respectively.The improved discoverers will perform a global search based on the size of the search dimension and the po-sition of the current optimal value,and some joiners will perform a global search according to the distance between the optimal position and themselves.The prediction effects of four short-term traffic flow prediction models,BP,PSO-BP,SSA-BP and ISSA-BP,are com-pared and analyzed through experiments.The results show that the error of ISSA-BP short-term traffic flow prediction model is the smallest,and the prediction accuracy of ISSA-BP model is much better,48.85%higher than that of BP model in terms of MAE evaluation in-dicators.
NETL
NSTL
万方数据
维普
