给出了模糊推理规则表。
然后，通过设计的模糊推理规则进行模式的分类。
Then patterns are categorized by the designed fuzzy inference regulation.
然后，通过设计的模糊推理规则进行模式的分类，这种分段线性分类器的设计提高了算法线性分类的能力。
Then patterns are categorized by the designed fuzzy inference regulation. The design of this piecewise linear classifier enhances the ability of linear classification of the algorithm.
基于这种模糊控制关系和模糊推理规则，得到了被测软件通过测试的标准值，使测试通过的判定得以量化；
The standard value of the tested software to pass the test was then obtained from the fuzzy control relation and fuzzy inference rules, thus quantifying the judgment for software to pass the test.
在得到了脸部信息的状态值后，进行驾驶员疲劳程度的综合评判，制作模糊控制器定义输入输出以及模糊推理规则。
Conduct a comprehensive evaluation driver fatigue by making the definition of input and output of fuzzy controller and fuzzy inference rules.
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.
本文论述了模糊子集的云模型表示、基于云模型控制规则的不确定性推理，并设计了一种二维云模型控制器。
This paper presents the cloud model of fuzzy sets, uncertainty reasoning of control rules based on cloud model, and a two-dimension cloud model controller.
并将模糊综合评判与产生式规则融合，利用复合的推理机制提高预测结果的准确性。
The hybrid inference mechanism included dynamic fuzzy comprehensive evaluation and production rules obviously improves the veracity of the forecast results.
模糊控制以语言控制规则实现其推理过程，属于典型的非线性控制。
Fuzzy controller practice the reasoning process by language control rules, it has typical nonlinear characteristic.
PTES的推理机制使用了可能性逻辑及模糊集合理论作为其逻辑基础，并以一种形式化的方法提供了处理非确定事实及非确定规则的能力。
The reasoning mechanism of PTES can deal with both uncertain facts and uncertain rules in a formal way by employing possibilistic logic and fuzzy set theory as its logical basis.
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.
当在知识库中搜索到相应的知识规则后，采用T - s模糊推理模型的求解策略进行求解。
When the knowledge rule is found in the knowledge files, T-S fuzzy inference model is applied to resolution of question.
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.
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。
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.
整个网络既有神经网络的学习能力，又有模糊系统的基于规则的推理能力，特别是对子类的自动聚类能力。
The whole network has not only the learning ability to neural network, but also the logic ability to fuzzy system based on rules, especially the automatic clustering ability to sub-class.
并提出了模糊规则的改进推理和自学习方法。
While the article proposes the improved inference and self-learning method of fuzzy rule.
在对采用合成推理规则进行模糊推理的机制进行分析的基础上，给出了利用插值法也可以得到相同结果的原理。
Based on the analysis of the mechanism of fuzzy reasoning by compositional rule of inference, the principle of how to achieve the same results with interpolation method is presented.
我们从训练事例中可以学习得到一组模糊产生规则，但这组规则对于相应训练事例的推理精度一般都有待提高。
We can learn a set of fuzzy production rules which are learned from training examples have poor reasoning accuracy with respect to the training examples.
并将模糊综合评判与产生式规则融合，利用复合推理机制提高预测结果的准确性。
The hybrid inference mechanism included dynamic fuzzy evaluation and production rules obviously improve the veracity of the forecast results.
模糊控制技术能利用控制规则，模拟人脑的推理过程，通过对输入量模糊化和综合推理，可以弱化数据不准确对系统的影响。
Fuzzy control technologies that apply control rules to simulating the inference process of man's brain can diminish the affect on the system by data inaccuracy.
当模糊产生式规则应用在近似推理过程中时，前件相同而后件不同的模糊规则之间往往存在交互影响。
When fuzzy production rules are used in approximate reasoning, there are interaction influences on rules that have the same consequent but different antecedent.
论文提出一种模糊强化学习算法，通过模糊推理系统将连续的状态空间映射到连续的动作空间，然后通过学习得到一个完整的规则库。
In this paper, we propose a fuzzy reinforcement algorithm, which map continuous state Spaces to continuous action Spaces by fuzzy inference system and then learn a rule base.
针对直觉模糊粗糙逻辑（IFRL）推理的规则库检验问题，提出了IFRL规则库的互作用性检验方法。
To the rule-bases checking issue with intuitionistic fuzzy rough logical(IFRL) reasoning, an interactivities checking approach to IFRL rule-bases is proposed.
首先，提出一种模糊Q学习算法，通过模糊推理系统将连续的状态空间映射到连续的动作空间，然后通过学习得到一个完整的规则库。
A fuzzy Q learning algorithm is proposed in this dissertation, which map continuous state Spaces to continuous action Spaces by fuzzy inference system and then learn a rule base.
应用单层神经网络可以学习多变量模糊控制规则中的未知参数．还可由它来实现多变量模糊推理过程。
The parameters of me fuzzy control rules of me controller can be learned by the learning slgorithm of the neural netowrk. and the inference process can be realized by the network.
给出了模糊滑模控制器的构成，以及模糊化、模糊规则与推理、反模糊化处理的方法。
The composition of the fuzzy controller and the control method of the fuzzy, fuzzy rule and reasoning, and back fuzzy were given.
利用传统的基于模糊推理或规则匹配的专家系统对其进行入侵检测已不能满足系统的实时性和准确度要求。
Simply use the traditional technique based on fuzzy reasoning or expert system in intrusion detection system can not satisfy real-time and accuracy requirements.
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.
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.
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.
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