A new voltage control equipment using fuzzy inference

Power system control equipment needs higher sensitivity and operational reliability. Advanced voltage control equipment is needed for reducing the frequency of tap changes and improving the characteristics (the relationship between the actual voltage and reference voltage) of the voltage to meet today's power system requirements. However, these objectives are in a trade-off relationship. Studies of voltage control derived from a knowledge base suitable for electric power systems can satisfy these objectives using fuzzy inference. Compared with corresponding conventional equipment, the new equipment improved the deviation of 30 min average voltage of 30 percent. This paper describes the design concept of new voltage control equipment using fuzzy inference. In addition, field test results are described along with rules of fuzzy inference, membership functions, and the deviation of 30 min average voltage through detailed simulation.     

可动凝胶深部调驱候选井筛选方法研究

根据可动凝胶深部调驱机理,结合注水井和对应油井的静、动态特征,筛选出了用于确定最佳调驱井的指标体系,并采用变权的模糊综合评判方法进行多因素定量评价。结果表明,用渗透率非均质性、吸水剖面非均质性、吸水强度、井口压降曲线、含水-采出程度对应关系、油水井连通程度、油层未动用程度及沉积相特征等指标来筛选调驱注入井可提高选井的可靠性,保证可动凝胶深部调驱的效果。     

基于FAHP的制导雷达技术性能评估方法研究

针对复杂使用条件下制导雷达系统综合技术性能的模糊性和不确定性,运用模糊层次分析法对制导雷达系统的技术性能进行了综合评估,解决了一般层次分析法标度复杂、判断矩阵一致性不易实现的问题,建立了多级评估体系模型,给出了确立各指标权重的方法.最后通过实例分析,证明了该方法的有效性和准确性.     

马尔柯夫链预测法在证券指数预测中的应用

介绍马尔柯夫链预测法的模糊模型，该模型以集合的模糊划分为依据摒弃了清晰划分的不足．从而保证预测结果的精确性。将该模型应用在证券指数的预测上，取得了绝对误差、相对误差较小的结果。     

自适应比例因子模糊控制器在异步电动机调速系统中的应用

为了降低转子电阻变化对异步电动机矢量控制系统性能的影响，本文把自适应比例因子模糊控制器应用到了矢量控制系统中。介绍了自适应比例因子模糊控制方法的綦本原理，给出了使用自适应比例因子模糊控制器的异步电动机矢量控制系统的结构框图，对主模糊控制器和辅助模糊控制器进行了设计。仿真实验结果表明，采用这种控制方法的调速系统不仅具有优良的动态性能，而且对转子电阻变化具有很强的鲁棒性。     

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.     

Controlling the losing probability in a monotone game

We deal with a complex game between Alice and Bob where each contender’s probability of victory grows monotonically by unknown amounts with the resources employed. For a fixed effort on Alice’s part, Bob increases his resources on the basis of the results for each round (victory, tie or defeat) with the aim of reducing the probability of defeat to below a given threshold. We read this goal in terms of computing a confidence interval for the probability of losing and realize that the moves in some contests may bring in an indeterminacy trap: in certain games Bob cannot simultaneously have both a low probability-of-defeat measure and a narrow confidence interval. We use the inferential mechanism called twisting argument to compute the above interval on the basis of two joint statistics. Careful use of such statistics allows us to avoid indeterminacy.     

Culture and group perception: Dispositional and stereotypic inferences about novel and national groups.

In 3 studies, the authors tested the hypothesis that Chinese participants would view social groups as more entitative than would Americans and, as a result, would be more likely to infer personality traits on the basis of group membership--that is, to stereotype. In Study 1, Chinese participants made stronger stereotypic trait inferences than Americans did on the basis of a target's membership in a fictitious group. Studies 2 and 3 showed that Chinese participants perceived diverse groups as more entitative and attributed more internally consistent dispositions to groups and their members. Guided by culturally based lay theories about the entitative nature of groups, Chinese participants may stereotype more readily than do Americans when group membership is available as a source of dispositional inference. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

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.     

虚拟环境下自主水下航行器的避障研究

为了在虚拟环境下模拟自主水下航行器的水平面避障过程，基于软件平台MultiGen Creator和Vega，在HP工作站上开发了自主水下航行器避障视景仿真系统。提出了一种前视声纳的仿真方法，利用Vega提供的碰撞矢量函数和自定义的Volume碰撞方式，模拟前视声纳的功能，并根据声纳所测得的障碍物相对于自主水下航行器的位置关系设计一个模糊推理系统来求解其避障角度，实现了虚拟环境下自主水下航行器的水平面避障。仿真结果表明，该视景仿真能够使仿真系统更加逼真和接近于实际情况，并且满足系统仿真的实时性要求。