Matrix Coding in GA and Its Application to Fuzzy Modelling
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摘要: 在遗传算法(GA)的实际应用中,许多问题都可以采用树结构描述.模糊建模中的结构辨识是指如何划分输入空间,它是一种复杂的非线性优化过程,模糊树模型可将输入空间的划分表示成二叉树结构的形式.本文提出了一种树结构的矩阵编码方法,这种编码方法直观、简单,非常适合遗传算法的各种遗传操作.针对模糊树模型,以树结构为个体,采用矩阵编码方式,利用遗传算法优化输入空间的划分,得到了一个精度较高而复杂度较低的次优模糊树模型.Abstract: In the practical applications of genetic algorithm(GA), a lot of problems can be described as tree structures. In fuzzy modelling, the structure identification of a fuzzy model is to partition the input domain, which is in essence a process of complicated nonlinear optimization. The partition of the input domain can be expressed as a binary tree by a fuzzy tree model. A matrix coding approach to representing a binary tree is presented, which is simple and suitable for the genetic operations in GA. The partition of the input domain is optimized by GA with matrix coding for tree-structured individuals and theresulting suboptimal fuzzy tree model is obtained, which has higher precision and lower complexity of model. A simulation example is given to validate the proposed method.
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Key words:
- fuzzy models /
- optimization /
- non-linear /
- fuzzy trees /
- genetic algorithm
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