基于神经网络的提升机衬垫摩擦系数的模糊综合评判

Fuzzy Comprehensive Evaluation of Friction Coefficient of Mine Hoist Lining Based on Neural Network

模糊综合评判,考虑多个相关因素,把评价因素集中的元素映射为评价结果集中相应的元素,从而对某一对象进行恰如其分的评价。针对衬垫摩擦系数模糊评判中权重分配存在主观性的问题,利用神经网络的自学习能力,通过一些典型数据进行学习,自动改变权重,从而使综合评判结果更加准确。由实例和仿真结果表明,符合实际情况。

The fuzzy comprehensive evaluation considers many correlative factors and it transforms elements focused on evaluation factors into elements converged in evaluation results. Thereby it makes a proper evaluation for some object. Aiming at the subjectivity problem existed in weight allocation in fuzzy evaluation of lining friction coefficient, we automatically change the weights by means of the self-learning property of neural network with a great deal of typical data, so as to make the comprehensive evaluation more accurate. The simulation results and practical examples show that the comprehensive evaluation is effective.