基于改进遗传算法-反向传播神经网络的升降机健康评价研究

Elevator safety assessment based on IGA-BPNN

针对施工升降机安全问题,基于专家调查法、ReliefF算法与Pearson相关系数法,建立了升降机的健康评价指标体系。引用层次分析法计算了各健康指标的权重,基于三角模糊数对施工升降机的健康等级进行了划分;分析了粒子群算法(particle swarm optimization,PSO)、狼群算法(wolf pack algorithm,WPA)与改进遗传算法(improved genetic algorithm,IGA)对5个测试函数的计算,发现IGA具有更高的精确度与收敛速度;提出了一种基于改进的遗传算法-反向传播神经网络(improved genetic algorithm-back propagation neural network,IGA-BPNN)的施工升降机健康评价模型;基于IGA提出了一种自适应的交叉概率和变异概率计算策略,提高了GA寻找全局最优解的能力;用IGA对BP神经网络的初始权值和阈值进行了优化,形成了IGA-BPNN模型;分别用GA-BPNN与IGA-BPNN算法对施工升降机的健康等级进行了预测判断。研究结果表明:与GA-BPNN算法相比,IGA-BPNN算法在升降机的健康等级预测方面具有更高的正确率和精度。

Aiming at the safety problems of construction elevators,a health evaluation index system was established based on expert investigation method,ReliefF and Pearson.The weight of each health indicator was calculated by analytic hierarchy process,and the health grade of construction elevators was divided based on triangular fuzzy number.The analytical calculations of 5 test functions with Particle Swarm Optimization(PSO),Wolf Pack Algorithm(WPA)and Improved Genetic Algorithm(IGA)were compared.It was found that IGA has higher accuracy and convergence speed.A health assessment model for construction elevators was presented to solve those problems based on improved genetic algorithm-back propagation neural network(IGA-BPNN).An adaptive cross probability and mutation probability calculation strategy was proposed by IGA,which improved the ability of GA to find the global optimal solution.IGA was used to optimize the initial weights and thresholds of the BP neural network to form the IGA-BPNN model.GA-BPNN and IGA-BPNN were used to predict and judge the health level of construction elevators.The results indicate that the IGA-BPNN algorithm has higher accuracy and precision in predicting the health level of the elevator.