In this paper, the fuzzy membership function is built by analysis on design parameters' fuzzy attribution, and optimization values are obtained on the basis of fuzzy aggregate expand theory.
通过对设计参数的模糊分析建立模糊隶属函数，由模糊集合扩展理论求取设计参数的优化值。
By defining the membership function of the realization extent of each objective, the multi-objective optimization problem has been turned into a fuzzy programming problem to be solved.
通过对各目标实现程度的隶属函数进行定义，将多目标优化问题转变成模糊规划问题进行求解。
A new algorithm based on neural network models is also presented, in which the neural networks are employed to express the membership function of fuzzy sets and solve the optimization problems.
该算法分别采用神经网络模型进行模糊集隶属函数的表达及优化问题的求解，从而将模糊优化同神经网络有机地结合起来。
Then optimization model with the membership degree is constructed. It is solved by using genetic algorithms with dynamical castigatory function.
然后结合隶属度变量构建优化模型，利用具有动态惩罚函数的遗传算法求解，计算得到各方案的所属类别。
It is difficult to determine the membership function in fuzzy optimization method and to obtain the knowledge, experience and rules in expert system.
模糊优化算法中隶属函数的确定及专家系统中专家的知识、经验和规则的获取都是棘手的问题。
It is difficult to determine the membership function in fuzzy optimization method and to obtain the knowledge, experience and rules in expert system.
模糊优化算法中隶属函数的确定及专家系统中专家的知识、经验和规则的获取都是棘手的问题。
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