模糊推量的函数变换观点

基于函数论的立场，指出模糊推理过程是一个函数变换过程，模糊规则蕴涵了一个从函数空间到函数空间的映射，现存的种种模糊推理方法都是对这种映射的估计，进而指出插值和回归的方法都适用于这种估计。系统地提出了用回归的方法处理模糊推理的思想，并结俣线性回归模型进行了示范，证明了基于线性回归模型的模糊推理系统（FIS）同样是一个万能函数逼近器。     

基于模糊推理的贴近度决策方法及应用

利用模糊综合评判和模糊推理相结合，建立不同评价空间之间的映射，提出了基于模糊推理的贴近度决策方法，通过实例对比分析，验证基于贴近度的优选决策的结果也是合理的。     

基于模糊推理的软件质量评价模型

通过成立由开发商、用户和评价方组成的专家组,在国际通用的软件质量评价标准和模糊综合评价的基础上,建立的模糊规则,利用模糊推理技术,寻求不同评价空间之间的映射,构建了基于模糊推理的软件质量评价模型。该模型符合人们在处理复杂问题的逻辑思维方式,有利于合理准确地评价软件质量。     

基于模糊推理的权重优化

利用模糊推理技术建立了一种基于模糊推理的权重优化模型.该模型利用人们处理复杂问题的逻辑思维方式,通过评价空间和单位评价空间的映射,建立模糊关系方程并将其转化为优化问题得到权重.实验结果表明,利用该模型得到的权重能反映各因素的重要性,用其对待评对象进行模糊综合,结果也是合理的.     

应用自适应神经模糊推理系统(ANFIS)进行建模与仿真

模糊规划的提取和隶属度函数的学习是模糊推理系统设计中重要而困难的问题，自适应神经模糊推理系统（ANFIS）方法基于Sugeno模糊模型，其结构类似于神经网络，采用反向传播算法和最小二乘法调整模糊推理系统的参数，并能自动产生模糊规划，本文应用该方法给出了对一个典型系统建模的仿真实例，取得了良好的效果。     

多重多维模糊推理算法的连续性和逼近性

针对多重、多维模糊推理情形,细致地研究了几类模糊推理算法是否满足连续性和逼近性,并进一步讨论了这几类算法对逼近误差的传播性能。把模糊推理算法看成是一个模糊集合到另一个模糊集合的映射,选用海明距离作为两模糊集的距离度量方法,证明了在模糊假言推理和模糊拒取式推理情形,几类多重多维模糊算法都拥有连续性。当多重多维模糊算法满足还原性时就具有逼近性;该模糊算法都不会放大逼近误差。结果对构建模糊控制系统和模糊专家系统时选用和分析模糊推理算法有一定的指导作用。     

一种含控制滞后的Wiener模型的辨识方法

研究了含控制滞后Ｗｉｅｎｅｒ模型的辨识．在一定条件下，这一模型满足一组回归函数．利用Ｆｏｕｒｉｅｒ级数部分和可以求得回归函数在若干点上的估计．基于这些估计值可以决定控制滞后步数，进而辨识线性子系统和非线性映射的参数．仿真结果说明了算法的有效性     

增量式人体姿态映射模型的学习方法

判别式3D人体姿态估计方法直接学习图像观测到姿态之间的映射,需要大量训练集,而GPR对这种大训练集的映射模型学习由于计算复杂度太高而受到极大限制。提出了一种基于GPR和LWPR的增量式映射模型的学习方法,利用GPR学习各局部映射模型,基于LWPR的思想在线调整现有的模型和训练新的局部模型以及姿态估计。实验表明,该方法能够极大地减少大数据集上高斯过程回归的计算代价,并获得准确的姿态估计。     

参数生产前沿面分析的单边支持向量回归模型

为了解决了现有参数生产前沿面分析中先验生产函数难以选择的问题,提出参数生产前沿面分析的单边支持向量回归模型．该模型通过引入核方法,采用非线性映射将各生产决策单元的资源投入原始数据由数据空间映射到特征空间,然后在特征空间进行对应的线性操作。这样,则可以通过线性生产函数的非线性映射来解决生产函数的选择问题。最后,通过对珠三角各城市的经济发展效率进行评价,证明了该模型的有效性。     

模糊理论在预测函数控制上的应用

针对预测函数控制中模型失配的影响，提出了用模糊推理对控制量进行补偿的新型预测函数控制。并将基于模糊补偿的预测函数控制应用于锅炉燃烧控制系统，通过连续系统仿真，结果表明这种新型控制器具有较强的鲁棒性。     

利用神经网络进行推理的模糊控制器

本文介绍了一种利用神经网络进行推理的模糊控制器。网络的输入和输出均为模糊集。训练后的网络能完成合成关系，即模糊推时。为了减少ＢＰ网络的高线训练时间，对模糊集进行了“编码”。最后给出了该控制器应用于曲线环节的实时控制结果。     

基于模糊推理的教学策略推理机的算法设计

详细阐述了模糊推理系统与实现模糊推理机工作流程设计的方法和算法,给出基于一定方式结合的框架与规则知识表示的推理机算法和规则推理机设计思想及实现方法,为学生选择学习内容和学习方法时对教学策略做出调整.     

两类模糊推理算法的连续性和逼近性

对Zadeh的模糊推理合成法则(CRI算法)和全蕴涵三I算法(三I算法)是否满足连续性和逼近性问题进行了细致的研究,进一步讨论了这两类算法对逼近误差的传播性能.为此,把模糊推理算法看成是模糊集合到模糊集合的映射,选用海明距离作为两模糊集的距离.证明了在模糊假言推理和模糊拒取式推理情形,这两类算法都拥有连续性.指出三I算法在已知规则的前件和后件是正规集的条件下总是满足逼近性,而CRI算法只有当它满足还原性时才拥有逼近性.在满足逼近性的条件下,两类算法都不会放大逼近误差.结果对构建模糊控制系统和模糊专家系统时选用和分析模糊推理算法有一定的指导作用.     

Genetic tuning of fuzzy inference within fuzzy classifier systems

Abstract: In generating a suitable fuzzy classifier system, significant effort is often placed on the determination and the fine tuning of the fuzzy sets. However, in such systems little thought is given to the way in which membership functions are combined within the fuzzy rules. Often traditional fuzzy inference strategies are used which consequently provide no control over how strongly or weakly the inference is applied within these rules. Furthermore such strategies will allow no interaction between grades of membership. A number of theoretical fuzzy inference operators have been proposed for both regression and classification problems but they have not been investigated in the context of real-world applications. In this paper we propose a novel genetic algorithm framework for optimizing the strength of fuzzy inference operators concurrently with the tuning of membership functions for a given fuzzy classifier system. Each fuzzy system is generated using two well-established decision tree algorithms: C4.5 and CHAID. This will enable both classification and regression problems to be addressed within the framework. Each solution generated by the genetic algorithm will produce a set of fuzzy membership functions and also determine how strongly the inference will be applied within each fuzzy rule. We investigate several theoretical proven fuzzy inference techniques (T-norms) in the context of both classification and regression problems. The methodology proposed is applied to a number of real-world data sets in order to determine the effects of the simultaneous tuning of membership functions and inference parameters on the accuracy and robustness of fuzzy classifiers.     

基于T-S模糊推理优化KPI的应用

模糊系统是一种基于知识或基于规则的系统,它的核心就是由所谓的IF-THEN规则所组成的知识库.模糊推理就是针对给定的系统输入,综合运用知识库中的模糊推理规则,获得系统输出的过程.而T-S模糊模型的基本思想是将正常的模糊规则及其推理转换成一种数学表达形式.本文拟将绩效考核与模糊推理的优越性进行有效的结合,研究讨论出T-S模糊推理在绩效考核中的应用.以验证其收敛性及优越性.     

A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules

Fuzzy set theory has been used as an approach to deal with uncertainty in the supplier selection decision process. However, most studies limit applications of fuzzy set theory to outranking potential suppliers, not including a qualification stage in the decision process, in which non-compensatory types of decision rules can be used to reduce the set of potential suppliers. This paper presents a supplier selection decision method based on fuzzy inference that integrates both types of approaches: a non-compensatory rule for sorting in qualification stages and a compensatory rule for ranking in the final selection. Fuzzy inference rules model human reasoning and are embedded in the system, which is an advantage when compared to approaches that combine fuzzy set theory with multicriteria decision making methods. Fuzzy inference combined with a fuzzy rule-based classification method is used to categorize suppliers in qualification stages. Classes of supplier performance can be represented by linguistic terms, which allow decision makers to deal with subjectivity and to express qualification requirements in linguistic formats. Implementation of the proposed method and techniques were analyzed and discussed using an illustrative case. Three defuzzification operators were used in the final selection, yielding the same ranking. Factorial design was applied to test consistency and sensitivity of the inference rules. The findings reinforce the argument that including stages of qualification based on fuzzy inference and categorization makes this method especially useful for selecting from a large set of potential suppliers and also for first time purchase.     

A Fuzzy Inference Network Model for Search Strategy Using Neural Logic Network

In this paper, a fuzzy inference network model for search strategy using neural logic network is presented. The model describes search strategy, and neural logic network is used to search. Fuzzy logic can bring about appropriate inference results by ignoring some information in the reasoning process. Neural logic networks are powerful tools for the reasoning process but not appropriate for the logical reasoning. To model human knowledge, besides the reasoning process capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct a fuzzy inference network model based on the neural logic network, extending the existing rule inference network. And the traditional propagation rule is modified.     

基于模糊知识的混合推理技术研究与应用

针对模糊知识的内在联系,提出了一种模糊推理与逆向推理相结合的混合推理技术,介绍了该技术的设计思想,结合模糊知识实现了算法,重点论述了模糊推理与逆向推理相结合的推理过程,实现了一种较为理想的不确定性推理方法,有效地提高了推理机的执行效率。     

A variable spread fuzzy linear regression model with higher explanatory power and forecasting accuracy

Fuzzy regression models have been applied to operational research (OR) applications such as forecasting. Some of previous studies on fuzzy regression analysis obtain crisp regression coefficients for eliminating the problem of increasing spreads for the estimated fuzzy responses as the magnitude of the independent variable increases; however, they still cannot cope with the situation of decreasing or variable spreads. This paper proposes a three-phase method to construct the fuzzy regression model with variable spreads to resolve this problem. In the first phase, on the basis of the extension principle, the membership functions of the least-squares estimates of regression coefficients are constructed to conserve completely the fuzziness of observations. In the second phase, then they are defuzzified by the center of gravity method to obtain crisp regression coefficients. In the third phase, the error terms of the proposed model are determined by setting each estimated spread equals its corresponding observed spread. Furthermore, the Mamdani fuzzy inference system is adopted for improving the accuracy of its forecasts. Compared to the previous studies, the results from five examples and an application example of Japanese house prices show that the proposed fuzzy linear regression model has higher explanatory power and forecasting performance.