Study On The Road Tunnel Ventilation Control Method Based On Fuzzy Neural Network

The road tunnel ventilation system has strong non-linear characteristic and it is difficult to gain the precise mathematical model by using the traditional linear control theory, therefore, the modem control methods such as the fuzzy control become the trend for the road tunnel ventilation control. However, there are some difficulties with the establishment of the fuzzy membership functions and the rule base. The paper use Fuzzy Neural Network Control (FNNC) system to improve the method of tunnel ventilation control. Solved the problems such as Fuzzy Logical Control system's lacking of learning and adaptive ability, the membership functions of the fuzzy variables cannot be changed, the fuzzy logical rules cannot be modified automatically when environmental variables such as traffic model, average exhaust, etc, are changed.In view of the fuzzy control system of tunnel ventilation, on the basis of the best rule sorting independence, logical rules are modified and the optimized rules are gained. It takes much less time to regulate rules by using the above method than the traditional one. And established the tunnel hour traffic volume forecast model, in addition, membership functions are adjusted by using fuzzy neural network based on Back Propagation (BP) algorithm, so they match for the rules better and a more reasonable fuzzy combination is got. Then the final optimized fuzzy combination based on any specific traffic volume forecast model is gained. The FNNC system can modify the logical rules and regulate the parameters of membership functions automatically. Compared with the FLC system, the control process becomes more automatic, the time and the energy consumption becomes less.