隶属函数

烧碱浓度Fuzzy检测中隶属函数的确定

Determination of Membership Function for Fuzzy Measurement of Caustic Soda Concentration

B样条网络函数可以作为模糊神经系统的隶属函数。

B spline function can be used as the membership function in the fuzzy neural system .

B样条隶属函数的模糊神经网络研究

Research on Neural Fuzzy Network Using B Spline Membership function

用Fuzzy控制建立了相应隶属函数，确定了故障判别阈值。

Fuzzy control relationship is set up to form its membership function and determine its fault discrimination threshold .

改进的广义隶属函数法及其FPGA实现

Modified algorithm of generalized membership function and realized on FPGA

因此本设计选择B样条函数作为模糊神经网络控制器的隶属函数，以B样条函数为媒介将模糊控制与神经网络有机地结合起来，发挥各自优势，实现对控制器的优化。

So by combination of fuzzy control and neural-networks with B-Spline functions , can advance both advantages to control the system .

该方法采用B样条基函数作为模糊隶属函数，利用神经网络实现模糊推理，并采用反向误差传播算法对网络进行训练。

This method uses B-spline basis function as fuzzy membership function , neural network to realize fuzzy inference , and BP algorithm to train network .

隶属函数表明n维资源空间的一个点x属于生态位的程度，也表明物种对环境资源（点）的适应程度。

Membership of a point in the niche is equivalent to the adaptive degree of the species to the point of the environmental resources .

采用B样条函数作为模糊隶属函数，利用神经网络实现模糊推理，提出一种模糊B样条基函数神经网络，并将其用于交流伺服系统的控制。

A fuzzy B-spline function neural network is proposed to control AC servo system by using B-spline function as fuzzy membership function and using neural network to realize fuzzy interfernce .

应用模糊控制理论，构造了用于判断等级分类的隶属函数，从而实现了苹果的等级分类：分别标为A、B、C三类。

The theory of fuzzy control was used and the membership function was built to rate the grade of apples , which is fell into three grades : A , B , C.

本文介绍了传统PID控制、带非均匀隶属函数的模糊控制、带智能权函数的模糊控制三种控制方法。

The traditional PID control method , the fuzzy control method with unequable Membership Function and fuzzy control with intelligent weight function are introduced in the paper .

D.C.隶属函数模糊集及其应用（II）&D.C.隶属函数模糊集的万能逼近性

Fuzzy Set Based on D.C. Membership Functions and Its Applications ( II ) & Universal Approximation of D.C.Membership Function Fuzzy Sets

由RBF神经网络和遗传算法在线寻优模糊控制器的比例因子、模糊推理规则和隶属函数，并由RBF网络辨识被控对象的动态特性，以评价模糊控制器控制性能。

Dynamic identification model of controlling system is designed based on the RBF neural networks to appraise the controlling performance of fuzzy controller .

采用粗糙隶属函数计算规则可信度可减少确定可信度（CertaintyFactor，CF）的主观因素，使规则可信度更加精确客观。

Rough membership function is used to compute rule confidence , which diminishes subjective influences on confirming certainty factor ( CF ) and makes CF more accurate and objective .

使用MATLAB中的自适应神经模糊推理系统（ANFIS）可以更加准确地自动调整隶属函数参数。

The adaptive network-based fuzzy inference system ( ANFIS ) of MATLAB can help us to obtain a more accurate self-adaptive parameter of the membership function .

带有非线性隶属函数的FLP问题的求解

Solving the FLP Problem with Nonlinear Membership Function

通过最大化模糊隶属函数来确定投入与产出权重的上限和下限，以避免传统DEA模型中权重为0的缺陷。

Upper and lower bounds of input and output weights were determined through maximizing membership function in order to avoid drawback of zero weight in traditional DEA model .

指出在实际问题中普遍选用的三角形、半三角形、梯形、半梯形、高斯型、柯西型、S形、Z形、π形隶属函数模糊集等均为D。

In this paper , we will point out that the fuzzy sets based on triangular , trapezoid ,( Gauss ,) Cauchy , S-type , Z-type ,π - type membership functions are D.

采用隶属函数最大法设计控制器，克服了并行分配补偿法求解公共正定矩阵P的困难，用Lyapunov稳定性理论设计出确保模糊动态模型全局渐近稳定的新型控制器。

The controller is designed by using maximum membership which overcomes the difficulty in solving common matrix P. A new controller is designed by using Lyapunov theory to ensure the stability of fuzzy dynamic system is insured .

改进暂态算法新判据以及通过构造隶属函数进行多判据的信息融合，形成综合选线算法并使之与基于双CPU的硬件平台相结合。

Improve a faulty Line selection algorithm based on the information of Transient zero-sequence current and construct membership functions for multi-information fusion to form a comprehensive faulty line selection algorithm combined with hardware platform .

本文针对大洋性水团的T-S曲线族特征，提出了拟合其隶属函数的2种方法&直线定位法和坐标旋转法。

Two new methods of fitting the membership function of oceanic water masses are presented according to the characteristics of T S curves family of oceanic water masses .

以多元回归分析为依托，引入M相模糊论域，并建立LOGISTIC模糊隶属函数模型，最后应用该理论对实际问题做了分析。

With the introduction of M-phase fuzzy domain , here , based on the multivariate regression analysis , establishing the LOGISTIC affiliating degree math model which has been applied in a concrete problem in the following content .

建立模糊控制器逼近误差和控制器参数之间的线性关系，用Lyapunov稳定性理论设计参数的自适应律，不仅调节模糊规则结论参数，同时调节隶属函数的参数。

The linear relation-ship between the approach error and fuzzy controller parameters is established firstly . The parameter self-adaptive rule designed by Lyapunov stability theory adjusts both fuzzy rule conclusion parameters and membership function parameters .

分类识别中用模糊统计的方法对大量的样本车型特征参数处理后，构造了隶属函数，最后采用了D-S证据理论对车型进行分类。

Fuzzy statistical algorithm is adopted to construct membership function after handling a great quantity of vehicle type data , and finally vehicle type is classified by using D-S method .

该方法是基于TS模糊模型，采用三角形隶属函数计算给定样本的隶属度，利用稳态卡尔曼滤波器辨识模糊模型的结论参数。

It is based on T-S fuzzy model using triangle-shaped membership functions to calculate the grade of membership for each given pattern and using Kalman filtering to identify the consequent parameters of fuzzy model .

其中，模糊控制器输入量和输出量的隶属函数以及量化因子用遗传算法进行优化，并把该控制器用于一个基于PWM技术的直流位置控制系统。

With the genetic algorithm , the memberships of the fuzzy controller ′ s inputs and output , and the quantified coefficients are optimized . The controller is used to a DC position control system based on the PWM technology .

针对非线性系统，提出一种基于遗传算法的模糊预测控制，解决了T-S模糊模型的隶属函数不具有自适应性且模糊规则的确定具有复杂性和很大程度上的人为主观性。

A new fuzzy predictive control is introduced by using genetic algorithm to solve its membership functions without self-adaptability and complexity of fuzzy rules defined subjectively in T-S fuzzy model of model predictive control .

运用熵技术支持下的AHP法确定指标权重，选用模糊隶属函数进行指标量化，线性加权函数法建立各级诊断模型，诊断评判出小流域系统健康水平；

The method of AHP which were supported by the entropy technique was applied to fix the weight of index . The system health level is judged by integration diagnose which uses the fuzzy membership function and the linear weighted function .

首先选取训练集中最典型的一些样本，构造一个粗糙SVMs的分类超平面，用样本与这个超平面的相对距离定义隶属函数，将所有的训练样本都映射到一个带形区域；

Firstly , the learning machines select the most typical samples , such as the centers of two classes , to form the coarse classification hyperplane of SVMs named preformed hyperplane .

对非线性的TCP/IP网络拥塞控制系统进行了T-S模糊模型的建模，通过选取适当的模糊规则和隶属函数来提高拥塞控制系统的性能，并给出了理论性证明。

A T-S fuzzy modeling is done for the nonlinear TCP / IP network congestion control system . The performance of network congestion control system is improved by choosing appropriate fuzzy rules and membership functions , and the stability of the system is rigorously theoretic proved .