神经模糊系统中模糊规则的优选

提出一种基于两级聚类算法的自组织神经模糊系统，该系统采用两级聚类算法（改进的最近邻域聚类算法和Gustafson-Kessel模糊聚类算法）对输入／输出数据进行模糊聚类，并由模糊聚类的划分熵确定最优划分，建立模糊模型，模型精度可由梯度下降法进一步提高。仿真结果表明，这种神经模糊系统具有结构简单、规则数少、学习速度快以及建模精度高等特点。     

基于密度聚类的神经模糊系统建模算法

神经模糊系统经常被用来对非线性系统建模,并能取得很好的效果.以往的模糊系统建模方法存在着输入空间划分个数难以确定和规则冗余的问题,这些问题阻碍了模糊系统的应用.基于动态阈值DENCLUE和相似规则合并的神经模糊系统建模算法DDTSRM(DENCLUE using a dynamic threshold and similar rules merging),首先在DENCLUE算法中使用动态阈值来合并密度吸引子,得到DDT算法.DDTSRM利用DDT算法不依赖初始参数的特点,解决了输入空间划分个数难以确定的问题.因为DDT算法可以得到任意形状和任意密度的聚类结果的特性,所以提高了模糊系统模型的准确性.辨识出模型的初始结构后,DDTSRM通过计算模糊集合之间的相似度来减少规则冗余,使模糊系统模型结构得到优化.最后利用BP算法对系统模型进行训练,进而提高系统的建模精度.以S-Y模糊系统模型为原型,在两输入一输出的非线性函数和Box-Jenkins数据上的仿真实验证明了DDTSRM算法在神经模糊系统建模应用的有效性,能够取得精确的建模效果.     

基于改进模糊聚类与ANFIS的高速公路事件检测

为了准确并及时地发现高速公路上的交通事故隐患,减少事故引发的交通延迟,提高高速公路运行安全性,结合减法聚类与模糊C均值（FCM）聚类算法对输入样本数据进行聚类,建成初始模糊推理系统,然后通过神经网络的自学习机制,训练模糊系统参数,确定模糊推理规则,建立最终模糊模型。通过仿真实验结果对比,验证了基于改进模糊聚类与自适应神经模糊推理系统（ANFIS）建模方法的有效性。     

一种新的模糊聚类神经网络及其在语音信号系统辨识中的应用

主要解决语音信号模型的系统辨识问题.针对过去的模糊聚类算法进行系统辨识时逼近性能不理想的问题,提出了一种新的模糊聚类神经网络(FCNN).该方法以模糊系统模型为基础,将每个状态看作一个模糊系统,用连续的若干序列作为系统的输入,利用改进的模糊聚类辨识算法构成一种新型的模糊聚类神经网络,对系统的输出进行预测.通过语音信号系统辨识的实验,验证了本网络的有效性.     

基于应急事件响应的模糊聚类分析算法

介绍了模糊C均值聚类算法的实现途径，并针对这种算法对初值敏感的缺点，研究实现了一种已被应用的模糊C均值的自适应算法，即将用于聚类分析有效性评价的混合F统计量与算法结合在一起的一种改进算法。该算法能够自动确定最佳聚类数目，避免在聚类数目的选取上存在的主观性，解决了聚类中的全局最优问题，提高了算法的可靠程度。模糊聚类分析方法及有效性评估已在某市应急指挥系统项目中得到了应用。     

基于混合聚类算法的模糊函数系统辨识方法

针对传统模糊系统存在的结构难以确定和参数辨识复杂的问题,提出了一种基于混合聚类算法的模糊函数系统辨识算法.与一般的模糊函数系统相比,混合聚类算法结合模糊C均值和模糊C回归模型聚类算法的样本距离.在模型预测部分,采用高斯函数计算每个输入变量的隶属度,利用输入变量隶属度的模糊化算子得到输入向量的隶属度.应用于Box-Jenkins煤气炉数据、一个双入单出的非线性系统和Mackey-Glass混沌时间序列数据的试验结果表明,本文算法具有很好的辨识效果,从而验证了本文算法的有效性与实用性.     

监督模糊聚类神经网络及其应用

为在面料成衣之前客观评价其缝纫性能,提出了一种基于监督模糊聚类客观评价方法.通过引入输出空间对FCM聚类算法进行改进,同时反映输入空间的聚类特征和输出空间的逼近特性.用FAST系统测量服装面料的力学性能指标,运用核主成分法对所测指标进行分析,提取5个核主成分作为神经网络的输入.实验结果表明,系统可以根据中厚型棉织物的不同结构及物理性能快速准确地给出该织物成衣后的缝纫性能评价指标.     

一种基于聚类的超闭球模糊神经网络

针对一类不确定非线性多输入多输出复杂系统,根据系统的输入输出数据对,提出一种基于聚类的超闭球模糊神经网络系统．该系统通过改进的模糊聚类方法（FCM）确定模糊规则数,采用高维隶属度函数取代常规的单维隶属度函数,并对隶属度函数中心值和隶属度函数参数采用一步通过算法,所提方法可降低系统的模糊规则数,简化网络计算．此外,当系统的输入输出发生变化时,可实现模糊规则库的在线修改．仿真实例验证了所提方法的有效性．     

一种基于类覆盖和粒子群优化的模糊神经网络系统

提出一种基于类覆盖获取有向图和粒子群优化方法的模糊神经网络模式识别系统模型，该模型利用改进的贪心算法获得半径较均匀的超球体类覆盖，再利用超球体类覆盖实现模糊输入空间划分和模糊IF-THEN规则提取，以此实现模糊神经网络系统的结构辨识；采用改进的模糊加权型Mamdani推理法确定系统的输出，并使用基于粒子群优化的算法对系统参数进行精炼，使系统具有很好的强壮性和识别率．对11种矿泉水味觉信号的识别实验结果证明了该系统的可行性和有效性．     

关于模糊C-均值（FCM）聚类算法的改进

针对模糊C-均值（FCM）聚类算法的容易收敛于局部极值的不足,提出了一种改进的模糊FCM聚类算法,此新算法在聚类中心选取和优化过程中进行了充分的考虑,是一种用于确定最佳聚类数的聚类算法,并且利用了分阶段思想,结合动态直接聚类算法和标准聚类算法,来尽量避免模糊C-均值（FCM）聚类算法的不足。新算法与传统（FCM）聚类算法方法相比,提高了算法的寻优能力,并且迭代次数更少,在准确度上也有较大的提高,具有很好的实际应用价值。     

Development of a systematic methodology of fuzzy logic modeling

This paper proposes a systematic methodology of fuzzy logic modeling for complex system modeling. It has a unified parameterized reasoning formulation, an improved fuzzy clustering algorithm, and an efficient strategy of selecting significant system inputs and their membership functions. The reasoning mechanism introduces 4 parameters whose variation provides a continuous range of inference operation. As a result, we are no longer restricted to standard extremes in any step of reasoning. The fuzzy model itself can then adjust the reasoning process by optimizing the inference parameters based on input-output data. The fuzzy rules are generated through fuzzy c-means (FCM) clustering. Major bottlenecks are addressed and analytical solutions are suggested. We also address the classification process to extend the derived fuzzy partition to the entire output space. In order to select suitable input variables among a finite number of candidates (unlike traditional approaches) we suggest a new strategy through which dominant input parameters are assigned in one step and no iteration process is required. Furthermore, a clustering technique called fuzzy fine clustering is introduced to assign the input membership functions. In order to evaluate the proposed methodology, two examples-a nonlinear function and a gas furnace dynamic procedure-are investigated in detail. The significant improvement of the model is concluded compared to other fuzzy modeling approaches     

Fuzzy arithmetic-based interpolative reasoning for nonlinear dynamic fuzzy systems

FAIR (fuzzy arithmetic-based interpolative reasoning)—a fuzzy reasoning scheme based on fuzzy arithmetic, is presented here. Linguistic rules of the Mamdani type, with fuzzy numbers as consequents, are used in an inference mechanism similar to that of a Takagi–Sugeno model. The inference result is a weighted sum of fuzzy numbers, calculated by means of the extension principle. Both fuzzy and crisp inputs and outputs can be used, and the chaining of rule bases is supported without increasing the spread of the output fuzzy sets in each step. This provides a setting for modeling dynamic fuzzy systems using fuzzy recursion. The matching in the rule antecedents is done by means of a compatibility measure that can be selected to suit the application at hand. Different compatibility measures can be used for different antecedent variables, and reasoning with sparse rule bases is supported. The application of FAIR to the modeling of a nonlinear dynamic system based on a combination of knowledge-driven and data-driven approaches is presented as an example.     

Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable

The traditional regression analysis is usually applied to homogeneous observations. However, there are several real situations where the observations are not homogeneous. In these cases, by utilizing the traditional regression, we have a loss of performance in fitting terms. Then, for improving the goodness of fit, it is more suitable to apply the so-called clusterwise regression analysis. The aim of clusterwise linear regression analysis is to embed the techniques of clustering into regression analysis. In this way, the clustering methods are utilized for overcoming the heterogeneity problem in regression analysis. Furthermore, by integrating cluster analysis into the regression framework, the regression parameters (regression analysis) and membership degrees (cluster analysis) can be estimated simultaneously by optimizing one single objective function. In this paper the clusterwise linear regression has been analyzed in a fuzzy framework. In particular, a fuzzy clusterwise linear regression model (FCWLR model) with symmetrical fuzzy output and crisp input variables for performing fuzzy cluster analysis within a fuzzy linear regression framework is suggested. For measuring the goodness of fit of the suggested FCWLR model with fuzzy output, a fitting index is proposed. In order to illustrate the usefulness of FCWLR model in practice, several applications to artificial and real datasets are shown.     

直觉模糊神经网络的函数逼近能力

运用直觉模糊集理论,建立了自适应神经-直觉模糊推理系统（ANIFIS）的控制模型,并证明了该模型具有全局逼近性质.首先将Zadeh模糊推理神经网络变为直觉模糊推理网络,建立一个多输入单输出的T-S型ANIFIS模型;然后设计了系统变量的属性函数和推理规则,确定了各层的输入输出计算关系,以及系统输出结果的合成计算表达式;最后通过证明所建模型的输出结果计算式满足Stone-Weirstrass定理的3个假设条件,完成了该模型的全局逼近性证明.     

一种自适应神经模糊运动规划器设计

使用减法聚类和自适应神经模糊网络方法设计了一种水下机器人运动规划器。根据输入、输出数据的特性,用减法聚类算法,确定模糊系统的初始结构和参数,避免了模糊逻辑系统设计中隶属函数确定及模糊规则自动提取的盲目性和随机性。提出将神经模糊系统参数分解为非线性前提参数和线性结论参数并分开辨识。采用梯度下降算法和最小二乘算法分别进行自适应模糊推理系统前后件参数的优化。仿真结果表明:在相同的仿真环境下,所设计的自适应神经模糊运动规划器的规划效果要好于模糊运动规划器。     

模糊推理系统在DSP上的实现和优化

提出了一种便捷的模糊推理系统在DSP(数字信号处理器)上的实现方法,详述了利用MATLAB的模糊逻辑工具箱对模糊系统建模、算法模拟及在TMS320VC5509系统上的实现过程,对实现中遇到的问题给出了相应的解决方法。结合程序特点和硬件结构对代码进行了优化。通过实例证明,按此方法在DSP上实现的模糊推理系统和在MATLAB中算法模拟的输出一致,优化后代码的运行时间减少了约四分之一。     

Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques

In this paper, we present a new method for multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques. First, the proposed method constructs training samples based on the variation rates of the training data set and then uses the training samples to construct fuzzy rules by making use of the fuzzy C-means clustering algorithm, where each fuzzy rule corresponds to a given cluster. Then, we determine the weight of each fuzzy rule with respect to the input observations and use such weights to determine the predicted output, based on the multiple fuzzy rules interpolation scheme. We apply the proposed method to the temperature prediction problem and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) data. The experimental results show that the proposed method produces better forecasting results than several existing methods.     

强耦合多变量模糊温度控制系统的研究

针对强耦合多变量温度控制系统进行了研究，系统是以中央温度和均匀温度作为输入，以加热元件和热风速度作为输出，形成了双输入-双输出温度控制系统。采用具有解耦功能的模糊控制，介绍了控制算法的步骤，根据实际情况提出了分段控制算法。实践表明该系统切实可行，达到了所要求的控制精度和目的。     

Gaussian kernel-based fuzzy inference systems for high dimensional regression

We propose a novel architecture for a higher order fuzzy inference system (FIS) and develop a learning algorithm to build the FIS. The consequent part of the proposed FIS is expressed as a nonlinear combination of the input variables, which can be obtained by introducing an implicit mapping from the input space to a high dimensional feature space. The proposed learning algorithm consists of two phases. In the first phase, the antecedent fuzzy sets are estimated by the kernel-based fuzzy c-means clustering. In the second phase, the consequent parameters are identified by support vector machine whose kernel function is constructed by fuzzy membership functions and the Gaussian kernel. The performance of the proposed model is verified through several numerical examples generally used in fuzzy modeling. Comparative analysis shows that, compared with the zero-order fuzzy model, first-order fuzzy model, and polynomial fuzzy model, the proposed model exhibits higher accuracy, better generalization performance, and satisfactory robustness.