基于改进FPA的软件规模估算方法

规模度量是软件项目管理的关键,其度量的准确性直接关系到软件项目的成败。针对传统FPA方法中复杂度等级划分不连续性的问题,提出一种改进的复杂度权值计算方法。该方法利用模糊理论分析功能要素的复杂度,首先以DET和RET作为输入变量,以复杂度权值作为输出变量,建立模糊推理系统;然后根据FPA中复杂度和功能点数量的转换关系,设置模糊推理规则,并利用该规则确定复杂度权值。研究结果表明,这种模糊推理的方法可以消除不同复杂度等级之间的断层,从而使软件功能点的估算结果更加准确。     

基于T-S模糊模型的非线性噪声消除

噪声是影响系统辨识的不利因素,而实际系统不可避免的受到噪声的污染.对模糊推理系统在噪声消除中的应用进行了研究,提出了一种基于T-S模糊模型的模糊非线性噪声消除算法.说明了非线性噪声消除(NNC)的结构和使用NNC进行噪声抵消的原理.该方法由输入-输出数据对直接提取模糊规则,模糊规则的后件参数采用递推最小二乘法一次计算得出,然后从测量信号中消去噪声得到有用的信号.仿真结果表明模糊推理系统可以应用于噪声消除.     

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.     

基于扩展的带标识的Petri网的加权模糊推理

为了更加高效计算,并且更加便利地进行加权模糊推理来获得更多的有关加权模糊产生式规则的信息,提出一种某些库所中带有标识的模糊Petri网模型来进行加权模糊推理(WFR).用来标记模糊Petrl网运行的托肯值已经从[0,1]上的实数扩展到了模糊集合.提到的加权模糊推理(WFR)包括了局部权值,确定性因子和阈值等几种知识表示参数,参数用模糊数表示,通过提出的计算模糊推理结果的方法,可以更加高效地计算出最终的推理结果.     

模糊规则在线提取及其在非线性系统辨识中的应用

提出一种从输入-输出数据提取模糊规则的新方法基于K-Nearest-Neighbor概念对输入-输出数据对进行预处理;利用竞争学习对输入空间自适应聚类;提取高斯基;在线调整规则库和模糊推理.将该方法应用于非线性系统辨识,仿真结果表明了它的有效性     

应用带标识的模糊Petri网的模糊推理

本文针对模糊推理中常存在推理结果意义不明确的问题,提出应用带标识的模糊Petri网(MFPNs)进行模糊推理。推理的过程中考虑模糊产生式规则的权值、阈值、确定性因子等几种知识表示参数以获得更多信息。给出基于相似性测度的模糊推理算法,通过计算带标识的模糊Petri网的最终输出库所中的托肯值可以得到最终的模糊推理结果。通过实例可以验证这样得到的推理结果意义更明确,计算过程更加高效。     

规糊规则在线提取及其在非线性系统辨识中的应用

提出一种从输入－输出数据取模糊规则的新方法,基于K－Nearest-Neighbor概念对输入－输出数据对进行预处理;利用竞争学习对输入空间自适应聚类;提取高斯基;在线调整规则库和模糊推理,将该方法应用于非线性系统辨识,仿真结果表明了它的有效性。     

基于OWA算子的区间值加权模糊推理

针对如何对区间值模糊产生式规则赋予合理权值的问题,将OWA算子引入到区间值模糊推理中。介绍一种基于OWA算子的区间值赋权方法,根据此方法给出区间值模糊集上的加权模糊产生式规则的推理算法。在采用该算法的过程中,为合理地计算输入事实与规则前件的匹配程度,引入基于OWA算子的区间值模糊匹配函数值和总体贴近度的计算方法。实例分析表明了所给出的区间值模糊推理算法的有效性和可行性。     

基于模糊理论的pH值控制系统设计及应用

本文通过阐述模糊PID控制器精确量的模糊化、规则库的建立以及产生模糊推理,结合锌冶炼沉铁工艺过程pH调节出现的问题,提出了在西门子控制系统基础上应用SCL语言建立模糊PID控制器。用模糊控制理论将pH值的偏差和pH值的偏差变化作为输入变量,以输出增量作为输出语言变量,实践表明,通过该方法建立的模糊控制器具有很强的鲁棒性和可靠性。     

基于Sugeno型神经模糊系统的交通流状态预测算法

从交通流状态的模糊特性出发,设计基于Sugeno型神经模糊系统的交通流状态预测算法.选择交通流状态的影响指标作为模糊推理系统的输入、交通流状态作为输出;据经验对输入、输出划分模糊子集,给出相应的隶属度函数并制定模糊规则;建立具有5层结构的神经模糊推理系统,利用神经网络优化调整模糊推理系统的隶属度函数和模糊规则.仿真实验表明,神经网络可直接优化模糊推理系统的隶属度函数,通过对连接权值的训练间接优化模糊规则,故Sugeno型神经模糊系统相比常规模糊系统具有更好的交通流状态预测性能.     

A neural fuzzy system with fuzzy supervised learning

A neural fuzzy system learning with fuzzy training data (fuzzy if-then rules) is proposed in this paper. This system is able to process and learn numerical information as well as linguistic information. At first, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. We use alpha-level sets of fuzzy numbers to represent linguistic information. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Furthermore, they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, a fuzzy supervised learning algorithm is developed for the proposed system. It extends the normal supervised learning techniques to the learning problems where only linguistic teaching signals are available. The fuzzy supervised learning scheme can train the proposed system with desired fuzzy input-output pairs which are fuzzy numbers instead of the normal numerical values. With fuzzy supervised learning, the proposed system can be used for rule base concentration to reduce the number of rules in a fuzzy rule base. Simulation results are presented to illustrate the performance and applicability of the proposed system.     

Activation and Defuzzification Methods for Fuzzy Rule-Based Systems

In this paper, a Selective Inference Engine (SIE) is first proposed. SIE predicts the rules that will be fired, based on an anticipated location procedure, and then performs the inference calculations only on the latter. This anticipated location is based on the projection of the input data on the conditional space of the fuzzy system and the delimitation of the excited region. Then, the fired rules can be aggregated using the appropriate scheme. In the second part of this work, we propose new defuzzification methods which take into account the consequent membership function shapes.     

模糊推理的合成规则及其合成运算的选择研究

本文首先提出当知识库中含有多条取自于同论域上的模糊推理规则时，应用通常的六条合成推理规则所推得的结论将随着模糊推理规则数的增多而越来越偏离真实度这一问题。本文针对该问题对通常的六条合成推理规则及其三角模下的六条推广合成推理规则进行了理论比较研究，并对合成运算进行了比较研究。研究表明：合成推理规则（或Ｒ￣４）以合成运算ｍａｘ－Ｔ０是克服上述问题的最佳选择。这一重要结果对于模糊知识库的设计具有指导意义。     

Decomposition of a fuzzy controller based on the inference break-up method

A concept called the decomposition of multivariable control rules is presented. Fuzzy control is the application of the compositional rule of inference and it is shown how the inference of the rule base with complex rules can be reduced to the inference of a number of rule bases with simple rules. A fuzzy logic based controller is applied to a simple magnetic suspension system. The controller has proportional, integral and derivative separate parts which are tuned independently. This means that all parts have their own rule bases. By testing it was found that the fuzzy PID controller gives better performance over a typical operational range then a traditional linear PID controller. The magnetic suspension system and the contact-less optical position measurement system have been developed and applied for the comparative analysis of the real-time conventional PID control and the fuzzy control.     

Additive Fuzzy Systems: From Generalized Mixtures to Rule Continua

A generalized probability mixture density governs an additive fuzzy system. The fuzzy system's if‐then rules correspond to the mixed probability densities. An additive fuzzy system computes an output by adding its fired rules and then averaging the result. The mixture's convex structure yields Bayes theorems that give the probability of which rules fired or which combined fuzzy systems fired for a given input and output. The convex structure also results in new moment theorems and learning laws and new ways to both approximate functions and exactly represent them. The additive fuzzy system itself is just the first conditional moment of the generalized mixture density. The output is a convex combination of the centroids of the fired then‐part sets. The mixture's second moment defines the fuzzy system's conditional variance. It describes the inherent uncertainty in the fuzzy system's output due to rule interpolation. The mixture structure gives a natural way to combine fuzzy systems because mixing mixtures yields a new mixture. A separation theorem shows how fuzzy approximators combine with exact Watkins‐based two‐rule function representations in a higher‐level convex sum of the combined systems. Two mixed Gaussian densities with appropriate Watkins coefficients define a generalized mixture density such that the fuzzy system's output equals any given real‐valued function if the function is bounded and not constant. Statistical hill‐climbing algorithms can learn the generalized mixture from sample data. The mixture structure also extends finite rule bases to continuum‐many rules. Finite fuzzy systems suffer from exponential rule explosion because each input fires all their graph‐cover rules. The continuum system fires only a special random sample of rules based on Monte Carlo sampling from the system's mixture. Users can program the system by changing its wave‐like meta‐rules based on the location and shape of the mixed densities in the mixture. Such meta‐rules can help mitigate rule explosion. The meta‐rules grow only linearly with the number of mixed densities even though the underlying fuzzy if‐then rules can have high‐dimensional if‐part and then‐part fuzzy sets.     

用直觉模糊Petri网构建的供应链可靠性诊断模型

针对不断变化的供应链系统内外部环境因素,围绕供应链系统可靠性诊断问题,将直觉模糊集引入模糊Petri网建模,用直觉模糊数表示库所状态、变迁阈值和变迁输出置信度,构建了基于直觉模糊Petri网的供应链可靠性诊断模型。对直觉模糊产生式规则按照变迁激发前后变迁和库所之间的与或关系,将供应链可靠性诊断模型模糊推理规则划分为四种类型,得到了变迁触发前和触发后的与或直觉模糊推理规则。同时提出了相应的模糊推理算法,并通过实例验证了模型和算法的有效性,能够及时发现供应链系统故障。     

模糊元图:一种构造模糊知识库的新方法

在分析了现有模糊图论结构的基础上,对模糊超图和模糊有向图进行扩充,提出一种新的图论结构－模糊元图,并将其应用于飞行器邦联诊断中的模糊知识库构造,实际应用表明,基于模糊元图的知识库具有很高的推理效率,并且便于规则的添加和删除。     

A Strategy of Dynamic Reasoning in Knowledge-Based System with Fuzzy Production Rules

Fuzzy production rules have been successfully applied to represent uncertainty in a knowledge-based system. The knowledge organized as a knowledge base is static. On the other hand, a real system such as the stock market is dynamic in nature. Therefore we need a strategy to reflect the dynamic nature of a system when we make reasoning with a knowledge-based system.This paper proposes a strategy of dynamic reasoning that can be used to takes account the dynamic behavior of decision-making with the knowledge-based system consisted of fuzzy rules. A degree of match (DM) between actual input information and antecedent of a rule is represented by a value in interval [0, 1]. Weights of relative importance of attributes in a rule are obtained by the AHP (Analytic Hierarchy Process) method. Then these weights are applied as exponents for the DM, and the DMs in a rule are combined, with the Min operator, into a single DM for the rule. In this way, the importance of attributes of a rule, which can be changed from time to time, can be reflected to reasoning in knowledge-based system with fuzzy rules.With the proposed reasoning procedure, a decision maker can take his judgment on the given decision environment into a static knowledge base with fuzzy rules when he makes decision with the knowledge base. This procedure can be automated as a pre-processing system for fuzzy expert systems. Thereby the quality of decisions could be enhanced.     

Designing fuzzy inference systems from data: Aninterpretability-oriented review

Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation and system optimization. Rule generation leads to a basic system with a given space partitioning and the corresponding set of rules. System optimization can be done at various levels. Variable selection can be an overall selection or it can be managed rule by rule. Rule base optimization aims to select the most useful rules and to optimize rule conclusions. Space partitioning can be improved by adding or removing fuzzy sets and by tuning membership function parameters. Structure optimization is of a major importance: selecting variables, reducing the rule base and optimizing the number of fuzzy sets. Over the years, many methods have become available for designing FIS from data. Their efficiency is usually characterized by a numerical performance index. However, for human-computer cooperation another criterion is needed: the rule interpretability. An implicit assumption states that fuzzy rules are by nature easy to be interpreted. This could be wrong when dealing with complex multivariable systems or when the generated partitioning is meaningless for experts. The paper analyzes the main methods for automatic rule generation and structure optimization. They are grouped into several families and compared according to the rule interpretability criterion. For this purpose, three conditions for a set of rules to be interpretable are defined