Although there are many kinds of methods to optimize compute, it is difficult to artificially set fuzzy rules, even impossible.
尽管用各种方法进行优化计算，人工设定模糊规则还是很困难的，甚至是不可能的。
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。
On the basis of fuzzy clustering and rough set, an algorithm for extracting fuzzy rules was proposed.
提出了一种基于模糊软分类和粗糙集理论来提取模糊规则的一种算法。
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。
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。
The classical methods of extracting fuzzy rules is determined by experience, which is complex and more difficult.
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。
This article describes a new type of fuzzy system with interpolating capability to extract MISO fuzzy rules from input output sample data through learning.
描述了一个通过学习从输入输出采样数据中提取MISO模糊规则的具有插值性能的新型模糊系统。
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。
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。
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。
The learning algorithm and the characteristics of the fuzzy rules model which can approximate the experiment data are shown to converge to any arbitrary accuracy by the theoretical analysis.
理论分析说明这种模糊规则后件参数学习算法是收敛的、所建模糊模型能够以要求的精度逼近已知的实验数据。
Inverse dynamics fuzzy rules model is the mathematical model about object movement disciplinarian which can be used for fuzzy controller design directly.
系统逆动力学模糊规则模型，是一类可以直接用于模糊控制器设计的关于对象运动规律的数学模型。
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。
The article describes the design of a new type of intelligent PID fuzzy controller with an emphasis on automatic setting of PID parameters by use of fuzzy rules.
本文介绍了一种新型智能PID模糊控制器的设计方法，重点介绍了采用模糊规则自动调整PID控制器参数。
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。
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。
Under fuzzy environment, the fuzzy extension matrix approach can generate a set of fuzzy rules from examples according to the minimum fuzzy entropy criterion of the path.
在模糊环境下，模糊扩张矩阵算法根据路径的最小模糊信息熵标准，从示例中归纳产生一组模糊规则。
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。
Based on above work, in the third part, actualize fuzzy rules automatic generation applying fuzzy RBF neural net.
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。
By designing the fuzzy rules between the three parameters and the extent of insulator contamination, the fuzzy alarming method is used to improve the accuracy of alarm.
通过制定这三个参数与绝缘子污秽程度间的模糊规则，采用模糊判警的方法，提高报警的准确性。
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。
The fuzzy space and the number of fuzzy rules of this model are defined by the fuzzy clustering method and weight coefficients of the model are adjusted by the BP algorithm.
该模型利用模糊聚类技术确定系统的模糊空间和模糊规则数，利用BP算法调整模糊神经网络的权系数。
This is a method that combines PI control with fuzzy control, realizes on line approximately auto-regulating control of PI speed control parameter of hydraulic turbine governor based on fuzzy rules.
这是一种把PI控制与模糊控制有机地结合在一起的方法，能够实现水轮发电机组PI调速器参数按模糊规则近似进行在线自动调整的控制。
The fuzzy space structure of system and the number of fuzzy rules based on fuzzy competitive learning algorithm are determined and the fitness degree of each rule contrast to each sample is obtained.
基于竞争学习算法的模糊分类器确定系统的模糊空间和模糊规则数，并得出每个样本对每条规则的适用程度。
The fuzzy space structure of system and the number of fuzzy rules based on fuzzy competitive learning algorithm are determined and the fitness degree of each rule contrast to each sample is obtained.
基于竞争学习算法的模糊分类器确定系统的模糊空间和模糊规则数，并得出每个样本对每条规则的适用程度。
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