Elevator traffic pattern recognition (ETPR) is the prerequisite for effectively implementing the strategies of elevator group control system (EGCS). In view of the time-varying, nonlinear and uncertain characteristics of elevator traffic, an ETPR method based on fuzzy BP neural network with self-organizing map (SOM) algorithm is proposed, in which the fuzzy logic (FL) is introduced into BP neural network and, the SOM algorithm is employed to both determine the membership functions and merge the fuzzy rules. Thus as a result, the network structure is optimized, at the same time, the self-learning function of BP neural network enables the weighting coefficients of the FL membership functions to vary with different traffic patterns (TPs) and, the elevator traffic demand information is processed by fuzzy reasoning to realize ETPR and, therefore, to provide effective support to scheduling EGCS. Simulation experiments show the validity of the proposed method.
Elevator traffic demand, elevator traffic pattern recognition (ETPR), fuzzy neural network, expert experience, self-organizing map (SOM) algorithm
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