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Synthesis Method of Empirical Models Optimal by Complexity under Uncertainty Conditions

机译:不确定条件下复杂度最优经验模型的综合方法

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摘要

There was developed the synthesis method of optimal complexity models for conditions when the model variables are fuzzy values. The method is oriented to the class of polynomial models. The best models are selected by using criteria of regularity or displacement. The application of ideas of genetic algorithms gives the opportunity to eliminate the problem of large dimension which is characteristic of combinatorial method. The efficiency of the developed method was verified on industrial data that allowed one to synthesize the empirical model optimal by structure for drilling conditions.
机译:针对模型变量为模糊值时的条件,开发了最优复杂度模型的综合方法。该方法针对多项式模型的类别。通过使用规律性或位移标准选择最佳模型。遗传算法思想的应用为消除组合方法特有的大尺寸问题提供了机会。在工业数据上验证了所开发方法的效率,该数据使人们能够通过针对钻探条件的结构综合最优化的经验模型。

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