A ELEVATOR GROUP CONTROL METHOD BASED ON PARTICLE SWARM OPTIMIZATION AND NEURAL NETWORK

In today's city life this elevator group control (EGC) problem is related to many factors, such as stochastic user equilibrium, the number of customers, running condition, is the difficulty of analysis, design and control. In order to improve the operation efficiency and service quality elevator, optimization control strategy, and the elevator was investigated. A new elevator group control method and system based on RBF algorithm is described. The RBF neural network is applied to control strategy in call distribution landing the elevator. Particle swarm optimization (PSO) neural controller-the method. Some links of the weighted parameters radial basis function neural network can be modified and optimization algorithms, and on the basis of the elevator group control performance effect can be obtained. The simulation results verify the contains the effectiveness of the method. The results prove that the method is effective.