Importance

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Abstract

A rule base storing control rule sets predicts elevator group management performance, such as waiting time distribution, obtained when applying each rule set stored in the rule base to the current traffic situation, and selects a rule set in accordance with a performance prediction. In addition, a weight database stores weighting parameters of a neural network corresponding to the rule sets and performance learning measures for correcting the weighting parameters in accordance with learning by the neural network. As a result, the optimal rule set is applied at all times for group management control of the elevators to provide passengers with excellent service and to enhance prediction accuracy in correspondence with the actual operational situation of the elevators.

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First Claim