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.
着重讨论了知识的获取及模糊控制规则。
The knowledge acquisition and fuzzy control rules were discussed emphatically.
对于模糊规则的获取，传统的方法是凭经验来确定，这种方法复杂且难度大。
The classical methods of extracting fuzzy rules is determined by experience, which is complex and more difficult.
将粗糙集理论与模糊逻辑技术相结合，提出了一种通过测量数据来获取模糊控制规则的方法。
Combining rough set theory with fuzzy logic technology, this paper has presented a method of gaining fuzzy control rules based on measured data.
设计一种新型混合模糊神经推理系统，该系统仅从期望输入输出数据集即可达到获取知识、确定模糊初始规则基的目的。
A novel hybrid neural fuzzy inference system is presented. Only based on the desired input output data pairs, are the knowledge acquisition and initial fuzzy rule sets available.
第三部分，在前述工作基础上，用模糊rbf神经网络实现模糊规则的自动获取。
Based on above work, in the third part, actualize fuzzy rules automatic generation applying fuzzy RBF neural net.
这种自适应模糊控制器基于模糊推理规则自学习和自调整的控制算法，无需知道太多的专家控制规则，因此解决了制冷系统MIMO模糊推理规则难以获取的问题。
This adaptive fuzzy controller is based on fuzzy inference rules self-learning without needing so much expert control rules, which solves the problem of acquiring MIMO fuzzy inference rules.
将粗糙集理论与模糊逻辑技术相结合，提出了一种通过测量数据来获取模糊控制规则的方法。
The expression of fuzzy logic system is combined with the list of fuzzy control rules in this paper.
将粗糙集理论与模糊逻辑技术相结合，提出了一种通过测量数据来获取模糊控制规则的方法。
The expression of fuzzy logic system is combined with the list of fuzzy control rules in this paper.
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