再利用D - S联合规则结合模糊逻辑理论，得到融合后的信度函数分配，从而确定故障元件。
The fusion belief function assignment is gotten by using D-S rule and fuzzy logic theory, and fault component is found.
在此基础上，构造出了模糊控制器的输入输出语言变量值的隶属度函数和模糊控制规则。
So the membership function of input and output lingual variables and control rules are formed in this thesis.
因此，模糊规则集的自动生成及隶属度函数的在线调整对于现代焊接过程控制来说是迫切需要的。
Therefore, the automatic determination of the fuzzy rule and in-process adaptation of membership function are required for the advanced welding process control.
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.
编码过程中对比例因子、模糊控制规则和隶属度函数进行了统一编码，实现了多个因数的整体寻优。
In the coding process, scale factor, fuzzy-control rule and Subordination function are integrated to code in order to complete integral optimizing.
提出了一种基于传感器和模糊规则的智能机器人运动规划方法，该方法运用了基于调和函数分析的人工势能场原理。
This paper presents a sensor-based intelligent robot motion planning using fuzzy rules for the idea of artifical potential fields based on analytic harmonic functions.
根据水下焊接的特殊情况，设计了一种带修正函数的规则自调整模糊控制器。
In view of the special circumstances of the flux-cored underwater welding, a rule self-tuning fuzzy controller with modification function is designed here.
用此方法，可以优化确定系统输入和输出变量的隶属函数以及模糊控制规则。
By this means, the membership functions of input and output variables and the fuzzy control laws can be optimized.
并利用寻优获得的隶属函数和控制规则与PID算法、常规模糊控制算法控制同一被控对象的响应结果进行了对比分析。
At last, control performance of optimized control rule and membership function are compared with that of PID and traditional fuzzy control system.
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。
Applying Adaptive Neural-Fuzzy Inference System (ANFIS) can produce fuzzy rules and adjust membership functions automatically based on data without experience of experts.
当获得了足够的数据后，通过自适应神经网络模糊系统ANFIS来训练产生隶属度函数和模糊规则，即产生模糊控制器。
When obtaining plenty data, self-adapt neural network fuzzy control system ANFIS come into being subjection degree function and fuzzy rule, namely come into being fuzzy controller.
隶属函数和推理规则的确定是模糊推理的难点。
It is hard to determine the membership function and inference rules in fuzzy reasoning.
通过合理地确定模糊集、隶属度函数和模糊控制规则等，初步研究和设计了核电站稳压器的模糊控制器。
A fuzzy controller of nuclear power plant pressurizer is researched and designed tentatively by defining fuzzy set, membership function and fuzzy control rule properly.
都包括在内，总共有12个变量，磨削表面粗糙度的影响，并提出了7个主要的变量和模糊规则的隶属函数。
A total of 12 variables which have effect on the grinding surface roughness were included, and the membership functions of the 7 major variables of them and their fuzzy rules were presented.
提出了一种新的带权的区间值模糊产生式规则，给出了加权模糊匹配函数和一个区间值排序算法。
In this paper, we propose a new interval-valued fuzzy production rule with weight, and provide weighted fuzzy matching function and an algorithm of interval value ranking.
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.
在系统实现中，采用隶属函数来反映故障对象特征的模糊性和模糊关系，基于关系数据库进行知识的模糊表达，实现基于规则的模糊推理。
In the implement of system, we use membership function to mirror faults fuzzy and fuzzy concern, relational database to describe knowledge and rules, and perform ruled-based inference.
模糊逻辑系统的区域分割学习方法采用规则前件为不对称高斯型隶属度函数的模糊逻辑系统。
Fuzzy rule region-split method adopts the fuzzy system whose antecedents take the asymmetric Gauss function as membership function.
由W确定模糊隶属函数及模糊判决规则。
The fuzzy subordinate function and fuzzy decision rules are determined by W.
参数设计包括：量化因子及模糊集合、隶属函数和规则输出、解模糊。
A parameter selection for fuzzy controller is given which includes: the quantization factor, fuzzy set, membership function, the role output and the fuzzy clearness.
利用模糊集合的隶属度函数，模糊条件句和模糊控制规则，实现对一阶线性时滞系统的控制。
Using membership function of fuzzy set, fuzzy condition sentence and fuzzy control rules, the fuzzy controller completes the control of the first-order linear delay system.
运用最优控制方法设计融合函数以降低模糊控制器的维数，减少模糊控制规则数，进而提高模糊控制器的性能品质。
The dimensions of input varieties of a fuzzy controller are depressed by designing a fusion function using the optimization control theory, and it can reduce the rules of fuzzy greatly.
采用具有层次结构染色体编码方式的遗传算法来设计模糊控制器，实现了语言控制规则的自动生成和隶属函数参数的自动整定。
By using the genetic algorithms with hierarchical chromosome structures, rule generation and para - (meter) tuning of the fuzzy controller can be achieved simultaneously.
提出了一种解析规则模糊控制器的改进结构，并引入了能够动态调整模糊控制规则的修正函数；同时，通过遗传算法，实现了模糊控制器控制参数的组合优化设计。
An improved structure of analytic expression based fuzzy controller is proposed, and a modifying function capable of regulating the fuzzy control rules dynamically is introduced.
该控制器采用分段解析函数控制规则，函数运算结果直接作为输出控制量，简化了模糊处理过程，且使得输出控制量参数具有自调整能力。
Fuzzy Algorithm is proposed in this paper. The controller adopts subsection analysis function control rules. The result of the function computation directly ACTS as the output control variable.
文中探讨了一种用于提取模糊规则的RBF神经网络结构，提出了基于此网路结构的模糊隶属度函数学习算法，最后给出了用于验证该算法有效性的仿真实例。
The learning algorithm of membership function based on the RBF Neural Network is discussed and an example is given to demonstrate the validity of this algorithm.
运用最优控制方法设计融合函数以降低模糊控制器的维数，减少模糊控制规则数，进而提高模糊控制器的性能品质。
The dimensions of input varieties of a fuzzy controller are depressed by designing a fusion function using the optimization control theory , and the quality of the fuzzy controller was improved.
通过一种新学习算法的导出，并结合模糊逻辑系统中的模糊基函数，给出了一种带有通用规则库的模糊滑模自适应控制器。
Universal optimization for adjustable parameters of fuzzy based function is realized by using GA and finding adjust law of parameters in general designing adaptive controller is replaced.
同时针对遗传算法寻优生成的模糊规则存在跳变和规则曲面粗糙等缺陷，在目标函数中引入光滑因子，有效解决了模糊规则跳变现象。
In order to resolve the irregularity between the optimized rules and the coarseness of fuzzy rules' surface, a smoothness factor is applied to the objective function.
同时针对遗传算法寻优生成的模糊规则存在跳变和规则曲面粗糙等缺陷，在目标函数中引入光滑因子，有效解决了模糊规则跳变现象。
In order to resolve the irregularity between the optimized rules and the coarseness of fuzzy rules' surface, a smoothness factor is applied to the objective function.
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