Automatic generation of fuzzy rules is a key technique in fuzzy control.
模糊规则自动生成是模糊控制的关键性技术之一。
Applying Adaptive Neural-Fuzzy Inference System (ANFIS) can produce fuzzy rules and adjust membership functions automatically based on data without experience of experts.
自适应神经网络模糊推理系统(ANFIS)能基于数据建模，无须专家经验，自动产生模糊规则和调整隶属度函数。
The rule base including 49 fuzzy rules is also established.
还建立了包括49 个模糊规则的规则库。
The hyperbolic model can be easily derived from a set of fuzzy rules.
这种模型是一种模糊模型，可以很容易由几条模糊规则得出。
It is important to structure the fuzzy rules store in the fuzzy control system.
在模糊控制系统中，模糊规则库的构建是至关重要的。
The fuzzy controller with an intuitive structure has a small number of fuzzy rules.
该模糊控制器规则总数少，直观性强。
An adaptive control algorithm is given to adjust fuzzy rules to adapt system change.
同时还给出了一种自适应控制算法，利用它可以调整模糊规则以适应系统的时变性。
Hybrid fuzzy logic method used fuzzy rules and crisp data to identify a fuzzy logic system.
混合模糊逻辑方法就是利用模糊规则和对象输入输出的观测数据来辨识一个模糊逻辑系统。
The weights of the weighted fuzzy rules can be acquired by training the fuzzy neural network.
训练好的模糊神经网络即为一个模糊推理器。
The fuzzy rules of the factors are discussed and it predicts the ridership of the mass transit.
确定各因素的模糊规则，采用模糊推理预测公交分担率。
This paper discusses the interpolation of fuzzy rules and it's use in reduction of fuzzy system.
本文讨论模糊规则插值法及其在简化模糊决策系统中的应用。
This paper proposes a new method to find fuzzy rules using an improved clonal selection algorithm.
提出了利用改进的克隆选择算法发现模糊规则的方法。
By combination of expert experience and fuzzy identification, the fuzzy rules become more reliable.
在建立模糊规则时，应用了专家经验与模糊辨识相结合的方法，使规则更加合理可信。
An on-line adaptive tuning algorithm for the consequent parameters in the fuzzy rules is also designed.
给出了模糊规则后件参数的自适应学习方法。
Therefore, researching automatic generation of fuzzy rules has important values in the theory and application.
因此研究模糊规则的自动生成有着重要的理论和应用价值。
Using the method, the fuzzy rules and crisp data were preprocessed and the ideal initial parameters were given.
该方法对模糊规则和观测数据进行了预处理，给出系统模型的初始结构和参数。
Finally. Conclusionindicates that methods of fuzzy rules generation and fuzzy system tuning are very effective.
结论表明，所提出的模糊规则生成和模糊系统学习方法是行之有效的。
The controller can learn the fuzzy rules and can adjust the fuzzy rules with the change of system automatically.
该控制器可以自动学习模糊控制规则，并随系统的变化自动调节模糊控制规则。
Proposes a classification algorithm based on simplified fuzzy rules base combining fuzzy clustering with rough set.
结合模糊聚类和粗糙集提出了一种基于精简的模糊规则库分类算法。
It's important to extract an appropriate fuzzy rules set for multi-classification problems that have fuzzy variables.
对于具有多模糊特征变量的多分类问题，自动提取适当的模糊模式识别规则集至关重要。
In the algorithm maximum and minimum partly replace product and sum. The paper proposes a means to obtain the fuzzy rules, too.
此算法用取大取小运算部分代替网络的积和运算，同时提出一种获取重要规则的方法。
A soft sensor modeling method is presented which selects optimal fuzzy rules by tuning the radius of a subtractive cluster center.
提出了一种通过调整减法聚类半径优选模糊规则的软测量建模方法。
Aiming at the familiar SISO thermodynamic system, the general structural style for inverse dynamics fuzzy rules model is presented.
针对常见的单输入单输出的热力系统，提出了逆动力学模糊规则模型的一般结构形式。
Besides, the model updates fuzzy rules based on immune automatically and constantly to improve the ability of detecting new intrusions.
另外，模型自动更新了基于免疫的模糊规则，从而可以提高检测新的网络入侵的能力。
In the research of reasoning machine, a FNN reasoning method is used to solve the problem of collision and inefficiency in the fuzzy rules reasoning.
在对推理机制的研究中，采用模糊神经网络推理方法解决了模糊规则推理时存在的冲突和低效率问题。
Moreover, T-S model is used to adjust the dynamic fuzzy rules by the latter neural network, which can improve the adaptability of the control system.
采用T - S模型，由后件网络动态调整模糊规则，提高控制系统的适应性。
A function for measuring clustering validity based on the fuzzy clustering algorithm is defined with which the number of fuzzy rules can be determined.
在模糊聚类算法的基础上，提出了一个衡量聚类有效性的函数，以确定模糊规则的数目。
A function for measuring clustering validity based on the fuzzy clustering algorithm is defined with which the number of fuzzy rules can be determined.
在模糊聚类算法的基础上，提出了一个衡量聚类有效性的函数，以确定模糊规则的数目。
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