准连续波体制雷达最大熵方法的算法选择

准连续体制作为一种新型雷达体制以其固有的优越性给雷达界带来了巨大的冲击。其数字信号处理系统的开发是进一步完善该体制雷达的关键。本文针对数字信号处理系统中最大熵谱外推电路的算法选择进行了研究。     

一种基于Web文本聚类的用户兴趣发现模型的研究

研究了一种基于Web文本聚类的用户兴趣发现方法．他通过Web文档信息获取，文本的形式表示，以及Web文本聚类方法最终提取用户兴趣知识，并给出了一个设计模型。     

压缩奇偶相干态的熵压缩特性

用数值方法讨论了相干态强度、相位角和压缩参数对压缩奇偶相干态位置熵和动量熵压缩特性的影响。结果表明，压缩奇偶相干态的位置熵和动量熵均可呈现压缩性，压缩偶相干态的最大熵压缩度比压缩奇相干态的要大。熵压缩度与相干态强度、相位角和压缩参数均有关系，但压缩因子对场熵压缩的影响最大。当其它条件一定时，压缩偶相干态在压缩参数|β|=O．95时，位置熵(或动量熵)的压缩度最大，但压缩奇相干态无此特点。     

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.     

Studies of substrate specificities of lipases from different sources

Although lipases are widely applied in a wide variety of reactions, available information on the factors that are responsible for the substrate specificities of lipases from different sources is scarce. In this paper, nine lipase‐producing microorganism strains were isolated from oil‐containing samples. The properties of these enzymes, including pH optima, temperature optima, thermal stability, and pH stability, vary significantly with the different sources from which these lipases were isolated. The substrate specificities of the nine lipases, including fatty acid and positional specificities, were also studied. The fatty acid specificities of the nine lipases were observably different toward 15 substrates with different carbon chain lengths, different saturation degrees and different side chains. The lipases from S₃ Penicillium citrinum (PCL), MJ₁ Aspergillus niger (ANL), MJ₂ Aspergillus oryzae (AOL), YM Bacillus coughing (BCL), S₉ Geotrichum candidum (GCL), and S₁₁ Candida lypolytica (CLL) showed the strongest specificities to short‐chain esters, and the other lipases showed strong selectivity for medium‐ or long‐chain and branched esters. The positional specificities were examined by analyzing the hydrolytic products of triolein catalyzed by the nine lipases, using TLC. The lipases can be mainly classified into two groups by their specificities for the ester bond at position 2 of triglycerides; one group can selectively hydrolyze the ester bond at position 2 of the triglycerides, the other group cannot. All these nine lipases were divided into four groups by hierarchical clustering analysis on the basis of the results of fatty acid and positional specificities.     

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。