Fuzzy modeling for intelligent decision making under uncertainty

We consider here the problem of decision making under uncertainty. We suggest an approach for the construction of decision functions which allow for the inclusion of probabilistic information as well as for the inclusion of information about the decision maker's attitude and preferences. Use is made of the fuzzy modeling technology to construct these functions from specifications provided by the decision maker.     

基于模糊免疫算法的溶剂脱水塔软测量

在实数编码免疫算法的基础上引进了模糊控制技术,对免疫算法中的两个关键参数实现了模糊自适应调整,很好地解决了基本免疫算法中收敛精度低和寻优速度慢的缺点。模糊免疫算法被用于优化BP神经网络的结构和参数。结果表明,不但网络的结构得到控制,而且泛化性能有了较大的提高;同时,算法在优化神经网络上的有效性也在溶剂脱水塔醋酸浓度软测量建模中得到很好地证实。     

Fuzzy modeling based on generalized conjunction operations

An approach to fuzzy modeling based on the tuning of parametric conjunction operations is proposed. First, some methods for the construction of parametric generalized conjunction operations simpler than the known parametric classes of conjunctions are considered and discussed. Second, several examples of function approximation by fuzzy models, based on the tuning of the parameters of the new conjunction operations, are given and their approximation performances are compared with the approaches based on a tuning of membership functions and other approaches proposed in the literature. It is seen that the tuning of the conjunction operations can be used for obtaining fuzzy models with a sufficiently good performance when the tuning of membership functions is not possible or not desirable.     

OWL-FC: an upper ontology for semantic modeling of Fuzzy Control

This work introduces an OWL-based upper ontology, called OWL-FC (Ontology Web Language for Fuzzy Control), capable to support a semantic definition of Fuzzy Control. It focuses on the fuzzy rules representation by providing domain independent ontology, supporting interoperability and favoring domain ontologies re-usability. The main contribution is that OWL-FC exploits Fuzzy Logic in OWL to model vagueness and uncertainty of the real world. Moreover, OWL-FC enables automatic discovery and execution of fuzzy controllers, by means of context aware parameter setting: appropriate controllers can be activated, depending on the parameters proactively identified in the work environment. In fact, the semantic modeling of concepts allows the characterization of constraints and restrictions for the identification of the right matches between concepts and individuals. OWL-FC ontology provides a wide, semantic-based interoperability among different domain ontologies, through the specification of fuzzy concepts, independently by the application domain. Then, OWL-FC is coherent to the Semantic Web infrastructure and avoids inconsistencies in the ontology.     

直链聚合物的结构性质关系的模糊集理论方法建模

将模糊集理论方法用于直链聚合物的结构性质关系建模,构造了两个模型。一个是直链聚合物的基团法描述的结构参数与其12种性质间定量关系的模型(模型lF);一个是直链聚合物的连接性指数描述的结构参数与其12种性质间定量关系的模型(模型2F)。两个模型给出的12种性质的拟合误差(拟合值与实验值间的标准偏差)分别是：V(298K)为15．1(模型lF)／58．4(模型2F)cc／mole,Ecch为6．580/17．838KJ／mole,δ为0．8225／3．2168(J／cc)^0.5,Fd为274／491J^0.5cm^1.5／mole,Tg为30／77K,Pt为23／66(cc／mole)(dyn／cm)^1/4,n为0．0125／0．095l,ξ为9／24 10^-6 cc／mole,UR为570／1240cm^l0／3(sec^1/3 mole),UR为549／1428cm^10/3／(sec^1/3 mole),Hqkam为5l1／1729gJ^1/3／mole^4/3,Yd,1／2为10．5／27．1K^*kg／mole。结果表明,所建立的模型可用于预测,且为聚合物结构性质关系建模引入了一个新的数学工具。     

改进的简化粒子群算法优化模糊神经网络建模

为了更准确地描述有记忆效应的射频功放特性,提出了一种改进的简化粒子群优化(PSO)算法,并结合自适应模糊推理系统(ANFIS)建立模糊神经网络功放模型.改进的简化PSO算法仅保留粒子的位置项,加入了随机的个体最优候选解,由粒子的当前位置、个体最优解、全局最优解和随机的个体最优候选解共同决定其位置项;采用线性递减惯性权重,并利用异步变化的动态学习因子,且新颖地引入拉普拉斯系数,从而增加了种群多样性,加快了收敛速度,避免陷入局部最优.由模型仿真对比可知,该方法建立的功放模型结构简单、收敛快、误差小、精度高,从而验证了建模方法的有效性和可靠性.     

Fuzzy techniques and user modeling in Sales Assistants

Uncertainty and fuzziness are ubiquitous in the field of computerized selling. Therefore the mastery of these domains might be a key factor for the success of electronic selling. In this paper the Sales Assistant is introduced. It employs user models in the problem solving and dialog control layers, and fuzzy techniques for the management of imprecision. Fuzzy Multiple Criteria Analysis has proven its usefulness in product evaluation if there are no severe interdependencies among the product attributes. The user model in the Sales Assistant is constructed unobtrusively on the basis of user behavior, and it uses short-term information. It increases the transparency and usability of a large hypertext-like information system.     

Fuzzy possibilistic modeling and sensitivity analysis for optimal fuel gas scheduling in refinery

In refinery, fuel gas which is continuously generated during the production process is one of the most important energy sources. Optimal scheduling of fuel gas system helps the refinery to achieve energy cost reduction and cleaner production. However, imprecise natures in the system, such as prediction of production rate of fuel gas, prediction of energy demand of the equipments and cost coefficient in the objective function, make the deterministic optimization method which requires well-defined and precise data cannot be competent for the fuel gas scheduling problem. In this study, fuzzy possibilistic programming (FPP) method is proposed to deal with these imprecise natures by triangular possibility distributions. The fuzzy possibilistic model is transformed into usual mathematical model by definition of necessity measure and the α-level method. Although FPP models have been widely applied to modeling, few research works have been reported on the performance evaluation, namely sensitivity analysis, of these models. Marginal value analysis, which is always used to provide additional economic information, is proposed to give the sensitivity analysis in the paper. This method is demonstrated to be much more flexible than the simulation method. Particularly, the analytical method is adopted to examine how the imprecise natures in the fuel gas system affect the scheduling results.     

Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty

Nowadays healthcare becoming more important aspect for everybody. Healthcare institutions now giving more attention to their patients’ safety by reducing the frequency of medical errors and trying to provide all kinds of best facilities to them. Clinical processes can be understood as a series of interactions between patients, providers, and technologies. Therefore, there are some chances exist for medical errors due to the involvement of human beings and machines. A number of tools exist to prospectively analyze processes in healthcare which generally needs precise numerical data. In general, available or extracted data is not precise and sufficient to assess the clinical processes upto a desired degree of accuracy due to various practical and economic reasons. Thus, collected data may have some sort of uncertainties and quantification of these uncertainties should be done very carefully before analysing further. In this paper, a new fuzzy fault tree approach has been presented for patient safety risk modeling in healthcare. This approach applies fault-tree, trapezoidal fuzzy numbers, α-cut set, and the weakest-t-norm (T_ω) based approximate arithmetic operations to obtain fuzzy failure probability of the system. The effectiveness of the developed approach is illustrated with two different kinds of problems taken from literature related to healthcare. Also, Tanaka et al.'s approach has been used to rank the critical basic events of the considered problems. Computed results have been compared with results obtained from other existing techniques.     

Fuzzy inference system to modeling of crossflow milk ultrafiltration

Fuzzy inference systems (FISs) have been adopted as a powerful tool to adequately model and simulate the crossflow ultrafiltration of milk. It primary aim of this research was to predict the permeate flux, total hydraulic resistance and the milk components rejection as a function of different physicochemical properties. Dynamic modeling of ultrafiltration performance of colloidal systems (such as milk) is an important criterion in designing of a new process, due to the complex nature of milk in a dynamic manner. Such processes exhibit complex nonlinear behavior due to unknown interactions between compounds and colloidal system, thus the theoretical approaches were not being able do successfully model and simulate the processes. In addition, it has been attempted to test the FIS ability to predict new data that is not normally available. To obtain this objective, fuzzy output with a threshold of 1.2 was constructed in order to predict rejections from the limited number of accessible experimental data. The findings of this work also reveal that there exist an excellent agreement between the experimental data and predicted values. Furthermore, the experimental results shows that the total hydraulic resistance and solutes rejection (except for protein) increased significantly with time for every corresponding value of the hydrodynamic parameters. On the other hand, the permeate flux decreased sharply. However, the fat rejection does not change with time for every value of the pH.     

Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling

Co-evolutionary algorithms have received increased attention in the past few years within the domain of evolutionary computation. In this paper, we combine the search power of co-evolutionary computation with the expressive power of fuzzy systems, and introduce a novel algorithm, called Fuzzy CoCo (fuzzy cooperative coevolution). We demonstrate the efficacy of Fuzzy CoCo by applying it to a hard, real-world problem - breast cancer diagnosis, obtaining the best results to date while expending less computational effort than previous processes. Analyzing our results, we derive guidelines for setting the algorithm parameters given a (hard) problem to solve. We hope Fuzzy CoCo proves to be a powerful tool in the fuzzy modeler toolkit     

基于功能点分析测试设计充分性模糊评判建模

提出了一种基于软件功能点分析结果设计测试用例的观点,通过分析功能点指标和测试设计之间的关系,以度量数据为基础,运用模糊数学方法建立了测试设计充分性综合评判模型,有效地解决了测试设计充分性难以定量评价问题,同时也为软件测试设计提供了一种新的思路和方法,并以一个实例验证了该模型的可行性。     

Fuzzy modeling of the medical treatment effects of superoxide dismutase

Abstract: In this paper, the effect of superoxide dismutase (SOD) in treating certain types of oral diseases is evaluated with a fuzzy relation composition method and a fuzzy integral method. Evaluation results demonstrate that SOD is effective in treating oral diseases. It is found also that SOD and SOD-containing complex enzyme are proven treatments for certain types of oral diseases. The effects of some applications are compared and discussed.     

Fuzzy modeling of high-dimensional systems: complexity reductionand interpretability improvement

Fuzzy modeling of high-dimensional systems is a challenging topic. This paper proposes an effective approach to data-based fuzzy modeling of high-dimensional systems. An initial fuzzy rule system is generated based on the conclusion that optimal fuzzy rules cover extrema. Redundant rules are removed based on a fuzzy similarity measure. Then, the structure and parameters of the fuzzy system are optimized using a genetic algorithm and the gradient method. During optimization, rules that have a very low firing strength are deleted. Finally, interpretability of the fuzzy system is improved by fine training the fuzzy rules with regularization. The resulting fuzzy system generated by this method has the following distinct features: (1) the fuzzy system is quite simplified; (2) the fuzzy system is interpretable; and (3) the dependencies between the inputs and the output are clearly shown. This method has successfully been applied to a system that has 11 inputs and one output with 20 000 training data and 80 000 test data     

基于正交最小二乘和UD分解的模糊建模方法

通过改进模糊聚类方法确定模糊模型的前件结构,并对模糊推理关系矩阵进行正交最小二乘估计。通过分析正交向量在模型中贡献的大小确定聚类规则的有效性,然后采用基于UD分解的最小二乘确定模糊模型的后件参数,实现模糊模型的结构和参数的优化。该方法已成功地应用于Box-Jenkins煤气炉的数据系统建模。     

使用新混合模糊聚类算法的模糊系统建模方法

为了进一步提高模糊系统建立模型的精度,提出一种新的模糊系统算法ANFIS-HC-QPSO：采用一种混合型模糊聚类算法来对模糊系统的输入空间进行划分,每一个聚类通过高斯函数的拟合产生一个隶属度函数,即完成ANFIS系统的前件参数--隶属度函数参数的初始识别,通过具有量子行为的粒子群算法QPSO与最小二乘法优化前件参数,直至达到停机条件,最终得到ANFIS的前件及后件参数,从而得到满意的模糊系统模型。实验表明,AN-FIS-HC-QPSO算法与传统算法相比,能在只需较少模糊规则的前提下就使模糊系统达到更高的精度。