Elevator control apparatus using neural network to predict car direction reversal floor

Claim 1

An elevator control apparatus comprising: an input data conversion means for converting traffic state data including elevator cage positions, cage running directions, and calls to be responded, into data in the form usable as input data to a neural network; means for predicting a reversal floor including a neural network having a least an input layer for receiving input data from said input data conversion means, an output layer for outputting, as output data, data corresponding to the predicted reversal floors at which elevator cages are predicted to reverse their moving directions, and an intermediate layer disposed between said input layer and said output layer which simultaneously processes the neural network data having weighing coefficients, said reversal floor prediction means transmitting data corresponding to the floors at which said elevator cages are predicted to reverse their moving direction, whenever a landing place call is registered; an output data conversion means for converting the output data into data in a form usable for a predetermined control operation, means for detecting floors at which the cages are actually reversed; learning data forming means for storing the predicted reversal floors of the cages together with the input data at the time of prediction and the floors at which the cages are actually reversed as learning data at a predetermined point of time in a running period of the elevator; correction means for correcting the weighing coefficients of said reversal floor prediction means using the learning data; and means for controlling the operation of the cages on the basis of the converted output data.