模式识别催化剂生产调优

本文介绍如何将模式识别应用于生产调优,着重讨论了多指标(优化目标)因素的分级,提出了用模糊综合评价度量和依有序聚类最小损失函数准则划类,并相继进行变量与样本筛选、信息压缩、特征提取和模拟仿真获得优区操作条件,实施效果显著。     

Fixed point theorems on fuzzy normed linear spaces

In this paper, definitions of strongly fuzzy convergent sequence, l-fuzzy weakly convergent sequence and l-fuzzy weakly compact set are given in a fuzzy normed linear space. The concepts of fuzzy normal structure, fuzzy non-expansive mapping, uniformly convex fuzzy normed linear space are introduced and fixed point theorems for fuzzy non-expansive mappings are proved.     

Using memory and fuzzy rules in a co-operative multi-thread strategy for optimization

In this article, we analyze a co-operative multi-thread search-based optimization strategy, where each solver thread represents a different optimization algorithm (or the same one with different settings), and they are all controlled by a centralized co-ordinator. We also propose the use of memory to keep track of both the state of the individual threads and the obtained solutions. Based on this memory, a very simple fuzzy rule base is used to control the system behavior.We also present the results of three computational experiments. The first of these checks the strategy by comparing it with an independent search strategy and a sequential algorithm, and the superiority of the co-operative scheme is confirmed. The second analyzes how definition of the threads affects the quality of the results, and the importance of there being a balanced set between intensification and diversification is corroborated. The third explores the use of memory with two different fuzzy rules, and the results indicate that the best combination is to use memory together with two rules (solver dependent and solver independent ones) (although this combination should not be activated at the beginning of the search in order to avoid premature convergence).     

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.     

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.     

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).