A general class of simple majority decision rules based on linguistic opinions

In this paper we have introduced a class of decision rules related to simple majority, by considering individual intensities of preference. These intensities will be shown by means of linguistic labels. In order to compare the amount of opinion obtained by each alternative, we have considered the total ordered monoid generated by the sums of the original labels, according to an addition and an ordering. In this general framework different sets of linguistic labels can be employed and these sets can be represented by means of diverse mathematical objects. Moreover, on these mathematical representations of linguistic labels several orderings can be considered. Thus, flexibility is an important feature of this new class of group decision making procedures. Some examples of putting in practice the simple majority decision rules based on linguistic labels are provided, and the main properties of these voting systems are analyzed. It is worth emphasizing that these properties are satisfied for any total ordered monoid, regardless of the mathematical representation of linguistic labels or the ordering used to compare collective opinions.     

Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons

In this paper, we propose and investigate a new category of neurofuzzy networks—fuzzy polynomial neural networks (FPNN) endowed with fuzzy set-based polynomial neurons (FSPNs) We develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms (GAs) in particular. The conventional FPNNs developed so far are based on the mechanisms of self-organization, fuzzy neurocomputing, and evolutionary optimization. The design of the network exploits the FSPNs as well as the extended group method of data handling (GMDH). Let us stress that in the previous development strategies some essential parameters of the networks (such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables) being available within the network are provided by the designer in advance and kept fixed throughout the overall development process. This restriction may hamper a possibility of developing an optimal architecture of the model. The design proposed in this study addresses this issue. The augmented and genetically developed FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNNs. The GA-based design procedure being applied at each layer of the FPNN leads to the selection of the most suitable nodes (or FSPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation in which we use a number of modeling benchmarks—synthetic and experimental data being commonly used in fuzzy or neurofuzzy modeling. The obtained results demonstrate the superiority of the proposed networks over the models existing in the references.     

YGQC磁选机磁系调节系统的模糊综合评判

介绍了设计YGQC干式磁选机模糊综合评判的基本原理。     

Optimal biocompatible solvent design for a two-stage extractive fermentation process with cell recycling

In this study, crisp and fuzzy multiple-goal optimization approaches are respectively introduced to design an optimal biocompatible solvent to a two-stage extractive fermentation with cell recycling for ethanol production. When designing a biocompatible solvent for the extractive fermentation process, many issues, such as extractive efficiency, conversion, amount of solvent utilized and so on, have to be considered. An interactive multiple-goal design procedure is introduced to determine a trade-off result in order to satisfy such contradicted goals. Both approaches could be iterated to solve the interactive multiple-goal design problem in order to yield a trade-off result. However, the crisp optimization design is a tedious task that requires the designer to provide various pairs of the upper bounds for the design problem to obtain the corresponding solution. The fuzzy optimization approach is able to be trade-off several goals simultaneously and to yield the overall satisfactory grade for the product/process design problem.     

LonWorks总线在锅炉模糊控制系统中的应用

采用LonWorks总线技术可以提高锅炉控制系统燃烧效率及可靠性、保证环保要求、降低成本、优化网络的热量分配。尤其多台锅炉通过LON总线联网运行能够合理分配负荷,从而达到提高工作效率的目的。     

A unified framework of opening and closure operators with respect to arbitrary fuzzy relations

 This paper is devoted to a general concept of openness and closedness with respect to arbitrary fuzzy relations – along with appropriate opening and closure operators. It is shown that the proposed framework unifies existing concepts, in particular, the one for fuzzy preorderings as well as the triangular norm-based approach to fuzzy mathematical morphology. The author acknowledges support of the K _plus Competence Center Program which is funded by the Austrian Government, the Province of Upper Austria, and the Chamber of Commerce of Upper Austria.     

SVM自适应波束成形算法

在天线阵列过载,以及强干扰与期望用户信号夹角过近的情况下,传统的线性阵列信号处理算法,如MMSE(minimum mean-squareerror)、NLMS(Normalized Least Mean Squares)等表现并不理想。SVM(SupportVectorMachine)是机器学习领域的最新成果,有较强的泛化能力,收敛快以及低复杂度等优点。本文提出了在上行波束成形中使用SVM算法,提高空域滤波的分辨率,与其他相关算法相比较,系统性能有了明显的改进。     

Determination of the total concentration and speciation of Al(III) in tea infusions

The aluminium species in different tea infusions were investigated, by determining their stability constants and concentration. This was done for some particular samples using a simple experimental method based on the sorption of aluminium on the strongly sorbing resin Chelex 100, by a batch procedure. From the thermodynamic information obtained it is possible to calculate the concentration of the different species, and in particular that of the free metal ion, which is very important for evaluating the adsorption of aluminium on biological membranes. It was found that aluminium in the tea infusions here considered is present at high total concentration, approximately 0.1 mM, but mainly linked to strong complexes, for instance with side reaction coefficient higher than 10(5.11) at pH 3.95 in one case (tea 1). This could be the reason for the low toxicity of aluminium in tea. These strong complexes were not dissociated even in the presence of Chelex 100. In this case only a limiting value of the reaction coefficient could be evaluated. The presence of the very strong complexes was found in all the tea sample here considered. In two of the considered samples (one black and one green tea) a part of Al(III) was linked to less strong complexes, for example with a reaction coefficient 10(4.14) (tea 2, pH 4.20). The presence in the considered tea infusions of other substances able to complex aluminium was also detected, by the well known ligand titration procedure, at concentration ranging from 0.65 to 3.37 mM in three tea infusions, and at somewhat higher concentration in the case of the ready drink, which was also considered for comparison.     

Tuning fuzzy power-system stabilizers in multi-machine systems by global optimization algorithms based on efficient domain partitions

In this paper, the problem of global tuning of fuzzy power-system stabilizers (FPSSs) present in a multi-machine power system in order to damp the power system oscillations is considered. In particular, it is formulated as a problem of global minimization of a multiextremal black-box function over a multidimensional hyperinterval. A global optimization technique, recently proposed, is used for solving the stated problem: the search hyperinterval is partitioned into smaller hyperintervals and the objective function is evaluated only at two vertices corresponding to the main diagonal of the generated hyperintervals, thus avoiding unnecessary ponderous simulations. Then, the performances of this technique are numerically compared with ones of a genetic algorithm (GA).     

基于Rough Set理论对原棉纱线强度的规则提取

纱线的强度和纤维的性能之间存在复杂的关系．利用纤维特性等级对实验所得的纱线强度和纤维性能的数据进行离散化处理，结合Rough Set理论知识，计算不同的纤维性能对纱线强度的影响因子，再基于约简后的离散化数据。提取数据中潜在的纱线强度和纤维性能之间的知识规则。