Abstract
The urban road tunnel longitudinal ventilation system has strong time-varying, non1inear and large delay characteristics and it is difficult to get the precise mathematical model, Conventional linear control theories are inefficient. RBF (Radial Basis Function) neural network is adopted in urban road tunnel longitudinal ventilation control system considering traffic flow, CO (Carbon monoxide), VI (Visibility) values and other factors. In normal traffic flow, density traffic flow, sparse traffic flow different situations for tunnel ventilation, test simulation results show that this control method is better and it is more efficient than the conventional method of nearly 15%.
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