模糊综合评价技术在住宅综合性能评估中的应用研究

依据《商品住宅性能评定方法和指标体系》正确地对商品住宅地性能进行评价 ,是评定商品住宅质量等级的重要依据。本文主要论述如何运用数学方法分析和处理住宅综合质量评定中大量存在的模糊问题以及如何建立相应的等级评价准则和进行等级评判     

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。     

Applicability of the flood‐pulse concept in a temperate floodplain river ecosystem: thermal and temporal components

Annual growth increments were calculated for blue catfish (Ictalurus furcatus) and flathead catfish (Pylodictis olivaris) from the lower Mississippi River (LMR) to assess hypothesized relationships between fish growth and floodplain inundation as predicted by the Flood‐Pulse Concept. Variation in catfish growth increment was high for all age classes of both species, and growth increments were not consistently related to various measures of floodplain inundation. However, relationships became stronger, and usually direct, when water temperature was integrated with area and duration of floodplain inundation. Relationships were significant for four of six age classes for blue catfish, a species known to utilize floodplain habitats. Though similar in direction, relationships were weaker for flathead catfish, which is considered a more riverine species. Our results indicate the Flood‐Pulse Concept applies more strongly to temperate floodplain‐river ecosystems when thermal aspects of flood pulses are considered. We recommend that future management of the LMR should consider ways to ‘recouple’ the annual flood and thermal cycles. An adaptive management approach will allow further determination of important processes affecting fisheries production in the LMR. Copyright © 2006 John Wiley & Sons, Ltd.     

基于概念网络的文本信息监控技术

由于网上信息的迅速增长,文本信息监控已经成为网上信息管理中的热点研究课题。本文在模糊集和语义网络的理论基础上,构建了模糊值动态约束性概念网络,介绍了该概念网络的基本构建方法,并提出了基于概念网络的文本分析算法。     

关于加强国土资源管理的思考

全文围绕树立和落实科学发展观这一主题 ,研究和探索如何在新时期、新阶段进一步做好国土资源管理工作的新思路、新途径和新方法。提出要进一步加强国土资源宏观管理 ,更好地为甘肃省经济社会的协调、可持续发展提供保障服务。     

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.