智能混合控制在高速纸机的应用

介绍了压力式网前箱的建模和控制，并通过构造一个新的控制器结构-智能混合控制器，将Fuzzy和PID控制融于其中。     

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.     

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.     

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.     

On a responsive replenishment system: a fuzzy logic approach

In today's competitive business environment, it is important that customers are able to obtain their preferred items in the shops they visit, particularly for convenience store chains such as 7–Eleven where popular items are expected to be readily available on the shelves of the stores for buyers. To minimize the cost of running such store chains, it is essential that stocks be kept to a minimum and at the same time large varieties of popular items are available for customers. In this respect, the replenishment system needs to be able to cope with the taxing demands of minimal inventory but at the same time keeping large varieties of needed items. This paper proposes a replenishment system which is able to respond to the fluctuating demands of customers and provide a timely supply of needed items in a cost–effective way. The proposed system embraces the principle of fuzzy logic which is able to deal with uncertainties by virtue of its fuzzy rules reasoning mechanism, thereby leveraging the responsiveness of the entire replenishment system for the chain stores. To validate the feasibility of the approach, a case study has been conducted in an emulated environment with promising results.     

基于模糊控制的气体流量控制系统

应用热式质量流量计直接测量气体质量流量。高性能AT89S51单片机作为控制器核心，并对控制量采用模糊控制算法。介绍了气体流量控制系统设计方法，给出了硬件原理图和软件主程序流程图。     

投产气井的模糊聚类分析

根据过去对投产气井分类问题的研究与应用效果，提出采用模糊数学的模糊聚类分析方法来研究投产气井的分类问题，通过编制计算机程序对陕甘宁中部气田１６口投产气井应用表明，分类结果更加合理。     

矿井串联系统模糊可靠性理论的研究

矿井系统不仅具有随机性，而且同时也具有模糊性，在系统分析与设计过程中应同时考虑这两种不确定性因素的影响。文中提出了模糊故障、模糊功能、系统模糊运营状态和广义可靠度等概念，并在此基础上提出了求单元及串联系统的生产能力、营运费用、应急维修费用、修理时间和广义可靠度等指标的方法，建立了使系统平均生产能力最大的系统最优运营策略的数学模型。