Abstract: A group-supervising control system for an elevator includes a cage position prediction unit for predicting cage positions after a predetermined time period based on the present positions, a service available time period distribution calculation unit for calculating the time periods until the service is available (predicted arrival times of a cage capable of responding to a hall call earliest) based on the predicted cage positions, and an assignment correction value calculation unit for calculating assignment correction values for correcting assignment estimation values based on the distributions of the time periods until the service is available. Unevenness in the time periods until the service is available with regard to the respective floors is decreased.

Abstract: The feature distinguishing part distinguishes feature modes from the traffic volume data detected by the traffic volume detecting part or from the traffic volume data estimated from the detected traffic volume data by the traffic volume estimating part, and the control parameter setting part sets the optimum control parameter according to the distinction results, further the drive controlling part controls the drive of cars on the control parameters. The distinction function constructing part constructs and modifies the distinction function of feature modes by learning prepared plural feature modes or the distinction results of past feature modes, furthermore the control result detecting part detects the control results or the drive results of cars, and corrects the control parameters. The control results or the drive results are exhibited on the user interface, and the control parameters are set and corrected from the outside by referring the results.

Abstract: A traffic volume estimating apparatus 1A estimates the traffic volumes of traffic apparatus, and a traffic flow presuming apparatus 1B presumes the traffic flows generating the estimated traffic volumes. A presumption function constructing apparatus 1C corrects the presumption functions of the traffic flow presuming apparatus 1B on actually measured traffic volumes, traffic flow presumption results and control results. A control result detecting apparatus 1G detects the control results and the drive results of the traffic apparatus. Further, a control parameter setting apparatus 1D sets control parameters on traffic flow presumption results, and corrects the control parameters according to the control results and the drive results.

Abstract: An elevator control apparatus capable of predicting reversion floors of elevator cages accurately. The control apparatus comprises a neural network, in which traffic state data are fetched into the neural network, so that predicted values of floors where the moving direction of each cage is reversed are calculated as predicted reversion floors. In the elevator control apparatus, reversion floors near true reversion floors can be predicted flexibly correspondingly to traffic state and traffic volume.