Traffic flow state-forecasting algorithm based on Sugeno neural fuzzy system
基于Sugeno型神经模糊系统的交通流状态预测算法

According to the fuzziness of traffic flow states, a traffic flow state-forecasting algorithm based on Sugeno neural fuzzy system(NFS) is proposed. In this algorithm, a number of traffic parameters are chosen as inputs, and the traffic flow states are taken as output of a NFS. The fuzzy subsets of inputs and output are given empirically. In addition, the corresponding membership functions and fuzzy IF-THEN rules are also built up by experience. A 5-layer NFS is presented in the given algorithm; and a neural network is used to optimize the fuzzy inference system(FIS). The experiment shows that neural network can optimize the membership functions directly and the fuzzy rules indirectly. Hence, the Sugeno NFS is more effective than the normal FIS in traffic flow state-forecasting.