基于支持向量机的制造系统静态复杂性综合模型研究

制造系统复杂性对优化制造系统结构设计、提高制造系统的生产效率具有非常重要的意义.然而目前对静态复杂性及动态复杂性影响因素复杂性及其测度的研究较多,对于如何将这些分因素复杂性进行综合得出综合复杂性的研究较少,这直接影响了复杂性研究结果的准确性。鉴于此,引入擅长处理小样本数据的支持向量机对制造系统静态复杂性进行综合,用遗传算法对其参数进行寻优,对模型的有效性进行了验证,结果说明方法的实用性与科学性较强.

Synthetizing Model for Static Complexity of Manufacturing System Based on SVM

The complexity of manufacturing system is important for structure design and produc-tion efficiency.Now,the researches on manufacture system are mainly focused on the influence factors’complexity and its measure and the research on complexity synthesizing method is rare. As the result of complexity is not very exact,the Support Vector Machine （SVM）which is good at dealing with small sample data is introduced to synthesize the subordinate complexity factors, and the genetic algorithm is applied to the parameters’optimizing.The effectiveness of this meth-od is validated and the result shows that this method is both practical and scientific.