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。
Aiming at the familiar SISO thermodynamic system, the general structural style for inverse dynamics fuzzy rules model is presented.
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。
Inverse dynamics fuzzy rules model is the mathematical model about object movement disciplinarian which can be used for fuzzy controller design directly.
提出了一个基于模糊隶属度和规则的分类层次诊断模型。
A hierarchic classification diagnosis model, based on fuzzy membership grade and rules is being proposed.
最后给出模糊控制规则模型自寻优优化方法。
The self-optimizing method of the fuzzy control rule model has been presented finally.
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。
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.
首先按过程输出随输入变量变化的程度对输入变量论域进行划分，在此基础上确定模糊模型的规则总数和前件参数；
The domain of discourse of input variables is divided firstly according to the changing degree of the process output while the input variables change.
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.
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.
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。
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.
该方法不依赖于系统的精确模型，将待控制量的误差及其变化率作为输入，根据控制要求建立模糊规则，实时输出比例、积分、微分系数的修正量，从而改善控制效果。
This method does not depend on the precise model, and establish the fuzzy rules under certain control requires. It can improve the Control results by adjusting the parameters in time.
并以工业电炉为控制对象，通过在线辨识模糊模型获得模糊控制规则，实现了模糊自适应控制。
Then with an industrial furnace as controlled object, fuzzy control rule is obtained and fuzzy self adaptive control is realized by on line identifying fuzzy model.
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。
Using the method, the fuzzy rules and crisp data were preprocessed and the ideal initial parameters were given.
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。
Besides, the model updates fuzzy rules based on immune automatically and constantly to improve the ability of detecting new intrusions.
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。
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.
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。
The hyperbolic model can be easily derived from a set of fuzzy rules.
制定模型的层次细节、损伤分级、纹理和分组规则，并采用模糊综合评判方法确定离散LOD等级。
Model rules on LOD, grades of damage, texture and grouping are constituted, and the fuzzy synthetically evaluation method is used to choose the level of discrete LOD.
建立多粒度时间序列的数学模型，并对提取模糊规则中所涉及的一些基本概念作出定义。
After the mathematical model of multiple granularity time series is established, some notations related to the rule discovering are defined.
建立了被控对象的模糊模型，在模糊模型的基础上，完成了模糊控制器的设计、模糊控制规则的建立。
On Basis of on fuzzy model that has been set up, the design of fuzzy controller and the rule of control have been finished.
我们还需要考虑的轮式移动机器人的运动学模型和模糊规则的输出，直接用车轮的速度。
We also take the kinematics model of wheeled mobile robot into account and use the speed of wheels directly as the output of fuzzy rules.
在模糊控制器中又通过自调整修正因子模糊数模型的在线插值方法提高了控制器规则的自调整功能。
And in the fuzzy controller on-line interpolation of the self-tuning gene fuzzy model is employed to improve the self-adjusting abilities of the control rules.
该模型无需事先确定模糊控制规则，并能通过神经网络的结构及参数学习调整模糊神经网络的结构。
By using this model, people need not select any fuzzy logic in advance, and can adjust the network structure by the structure and parameter learning of the neural network.
然后，采用最小二乘法求得T-S模糊模型的规则后件参数，从而建立起非线性系统的T-S模糊模型。
Secondly, the consequent parameters of rules are calculated by the least squares estimate. Thus, the T-S fuzzy model is set up.
提出了模型参考自组织模糊逻辑控制器的设计方法，并建立其模糊逻辑控制规则。
We propose the design method of model reference self organizing fuzzy logic controller, and construct its fuzzy logical control rules in this paper.
针对故障诊断过程，在数据融合与规则融合基础上，提出了灰色融合和模糊融合两种信息融合方法，并给出了并行决策融合模型。
Focused on fault diagnosis, a grey fusion method and a fuzzy fusion method are proposed based on data fusion and criteria fusion. Then the parallel decision fusion model is presented.
根据机器人的运动模型构造了模糊控制器，采用变长度编码方法对规则编码，减少了染色体的尺寸和复杂度，提高了学习速度。
The fuzzy controller is constructed based on the kinematic model of the robot. Messy coding method is used to reduce the size and complexity of chromosome and increase the learning speed.
在该模糊模型中，因各层的分割数相异，而使得该模糊规则具有不同特性。
In this fuzzy model, the fuzzy rules are generated on every fuzzy division layer with different fuzzy partitions from other layers.
由于T_S模糊模型每条规则的结论部分是一个线性模型，因此整个模糊模型可以看作一个线性时变系统，从而将模糊预测控制器中的非线性优化问题转化为一个线性二次寻优问题，以方便求解。
Since the conclusion part is linear, the T_S fuzzy model can be treated as a linear time_varying system, the nonlinear program in NMPC turns into a linear quadratic problem that can be easily solved.
该算法以一组模糊规则作为非线性对象内部模型，一条模糊规则表示一个局部线性系统；
The control scheme adopts a group of fuzzy rule sets as internal model of the nonlinear plant, where a fuzzy rule set represents a local linear system.
该算法以一组模糊规则作为非线性对象内部模型，一条模糊规则表示一个局部线性系统；
The control scheme adopts a group of fuzzy rule sets as internal model of the nonlinear plant, where a fuzzy rule set represents a local linear system.
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