引用本文:谭建豪,章兢.遗传算法在模糊设计中的应用[J].控制理论与应用,2010,27(4):501~504.[点击复制]
TAN Jian-hao,ZHANG Jing.Application of genetic algorithms in fuzzy design[J].Control Theory & Applications,2010,27(4):501~504.[点击复制]
遗传算法在模糊设计中的应用
Application of genetic algorithms in fuzzy design
摘要点击 2319  全文点击 1190  投稿时间：2008-12-13  修订日期：2009-05-10
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DOI编号  
  2010,27(4):501-504
中文关键词  模糊设计  遗传算法  模糊优化  回归方程  飞边尺寸
英文关键词  fuzzy design  genetic algorithm  fuzzy optimization  regression equation  flash size
基金项目  国家自然科学基金资助项目(60634020); 湖南省自然科学基金资助项目(08JJ3132).
作者单位E-mail
谭建豪* 湖南大学 电气与信息工程学院 tanjianhao96@sina.com.cn 
章兢 湖南大学 电气与信息工程学院  
中文摘要
      构造了CAD系统模糊设计的一种具体解决方案: 其环境为收集到的现场数据; 学习环节采用基于遗传算法的模糊优化算法; 知识库由设计准则构成; 执行部件为设计单元. 建立了回归方程的模糊优化学习算法, 并构造了该算法的流程. 然后利用该模糊设计系统获得了飞边尺寸设计准则, 且应用实例对该算法的稳定性进行了校验. 为评估该算法的性能, 将其与最小二乘法和免疫遗传算法进行了比较, 结果表明, 该算法速度快, 精度高, 稳定性好.
英文摘要
      A practical scheme of fuzzy design in CAD systems is developed, of which the environment is the currently collected data; the learning unit is the fuzzy optimization algorithm based on the genetic algorithms; the knowledge base is composed of design criteria; the executive part is the design unit. The fuzzy optimization learning algorithm of the regression equation is developed, and the corresponding flow chart is built. Then, the design criterion of a flash size is obtained by using this system; and the stability of the algorithm is verified through some examples. To evaluate the performances of the algorithm, we compare it with the least-squares method(LSM) and the immune-genetic algorithm(IGA); the result shows that our algorithm is faster, with higher precision and stability than the other algorithms.