基于遗传学习的可拓综合评价权重分配新方法

NEW METHOD FOR WEIGHTS ASSIGNMENT IN EXTENSION JUDGMENT BASED ON GENETIC ALGORITHMS

介绍了可拓综合评价和可拓故障诊断的特点。针对目前可拓综合评价中采用的各种权重分配方法不能跟踪环境变化的缺陷,提出了基于遗传学习的权重分配新算法。算法通过个体与环境的交互作用解决了在线权重分配问题。结合遗传学习的特点,给出了交互作用方式下的适应值函数的定义策略。同时研究了遗传算法中交叉和变异的自适应策略,并给出了适合在线权重分配遗传算法的参数。将该算法用于刀库可拓故障诊断权重分配中,结果表明算法具有较高的效率和精度。

The characteristics of extension synthetic judgment and extension trouble diagnosis are introduced. Then a new weights assignment algorithm is proposed to ravel out the limitation that most of exiting weights assignment algorithms used in extension synthetic judgment cannot work efficiently under the changing environments. The algorithm can solve online weights assignment problem through alternant influence between individuals and environment. A method is recommended to define the function of fitness of individual in genetic algorithm (GA) under the alternant manner according to the character of genetic learning. At the same time adaptive crossover and mutation strategy for GA is studied, and parameters for GA used in weights assignment algorithm are presented. The algorithm has been used in tool storage extenion trouble diagnosis and showed upper efficiency and precision.