Elevator control apparatus

A method of determining an estimated degree of capacity of an elevator car comprising the steps of:

• generating a user data signal including car position data, car direction data, car load data and data representing the statistical features of the traffic at the instant time, from general elevator car traffic data signal;
• passing the user signal through a neural net having first, second and third layers including a plurality of nodes, and having weighting factors disposed between the nodes, the neural net generating a plurality of outputs each output corresponding to the estimated car crowdedness at a given floor;
• storing data TA(f) representative of an actual car crowdedness at a hall corresponding to a car position f when a change in the car position is detected;
• calculating an error between the stored data TA(f) and corresponding outputs of the neural net;
• and
• adjusting the weighting factors in the neural net according to the calculated error.

An elevator control apparatus for estimating car crowdedness and for controlling operation of the car using the estimated car crowdedness, comprising:

• an elevator group control device which generates traffic data signals including car position data, car direction data, car load data and data regarding car calls and hall calls;
• an input data conversion means for converting the traffic data signals generated by said elevator group control device into a form which is usable as an input for a neural net;
• an estimated car crowdedness unit including a neural net having an input layer for receiving neural net data from said input data conversion means, an output layer for outputting a signal representative of the estimated car crowdedness of a selected car, and an intermediate layer provided between the input an output layers in which weighting factors are set;
• an output data conversion means for converting the signal output from the output layer into an operational control data signal;
• a learning data creation means including a memory unit for storing both the estimated car crowdedness for a predetermined hall and the input data at a predetermined time, for storing, as actual car crowdedness, the car crowdedness when said car stops at or passes the predetermined hall, and for outputting the stored input data, the estimated car crowdedness and the actual car crowdedness as a single learning data pair signal;
• and
• a correction means for correcting the weighting factors set in the intermediate layer of the estimated car crowdedness unit using the learning data signal.