在实际生活中，模糊目标函数和约束条件往往是非线性的。
In real life, fuzzy objective functions and constrains are always nonlinear.
根据模糊目标函数与模糊约束函数的关系，模糊优化问题可以分为对称和非对称两种类型。
According to the relations between the objective function and the constraint function, the fuzzy optimization can fall into tow categories about symmetry and unsymmetry.
新的目标准则函数考虑了数据集样本的模糊隶属关系和样本几何分布两个方面的因素，使算法的鲁棒性和分类的正确性大大加强。
Two factors are considered in cluster-validity criterion to enhance the robustness of algorithm and the validity of clustering, one is fuzzy membership and the other is geometric property.
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