| 摘要 |
This system represents an application of neural networks (NN1...NNm) to building traffic in elevator groups. Three neural network based traffic models (TM1,TM2,TM3) are provided to model, learn and predict passenger arrival rates (PAR) and passenger destination probabilities (PDP). Placed in a building, the models learn the traffic occurring by presenting their neural networks (NN1,NN2,NN3) with traffic data previously stored which is time at their inputs and arrival rates or car call distributions at their outputs. The neural networks (NN1,NN2,NN3) then adjust their internal structure to make historic predictions on data of the last day and realtime predictions on data of the last 10 minutes which are both combined in the combination circuit (11) to give optimum predictions. From every set of historic car calls and optimum arrival rates a matrix (7) is constructed, whose entries (8) represent the number of passengers behind a hall call with the same intended destination. The traffic predictions are used separately or in combination, by group control to improve cost computation and car allocation, thereby reducing the travelling and waiting times of current and future passengers. <IMAGE>
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