模糊控制系统的垂直切换判决算法分析

针对模糊控制系统中切换时延较长和切换次数较多的问题,研究了一种基于模糊控制系统的垂直切换判决算法。在基于信号强度判决时将RSS作为门限值,提高系统判决能力;同时,将网络参数和服务类型作为判决因素,结合层次分析法引入并行的模糊控制系统,缩短了切换判决的时间、选择适合用户的最佳网络,做出垂直切换。仿真结果表明,该算法减少了切换次数,降低了切换时延,增强了系统的性能。与传统的切换算法相比较,该算法对切换的判决因素考虑的更全面,兼顾了用户终端的使用环境及成本问题,有效地保证了网络的服务质量。     

Application of Attribute Reduct Method in Spatial Load Forecasting

Spatial load forecasting is a process distributing the total forecasted load to all partitioned area, and involving more spatial information and more factor influencing application of the future small area, which need a great deal of memory space and longer operation time. Rough set is new method of data analysis. It need not be provided with any advanced information except data set. But attribute reduct is its main algorithms. Division matrix approach on rough set used to reduce the attribute related to land - use decision in order to remove redundancy attribute and then the rules of small area land - use decision is distilled. The method obtained better effect and enhanced the total load forecasting efficiency.     

Fault Diagnosis Expert System of Combustion Engine with Rough Set Theory

On the basis of fault diagnosis expert system, the rough set theory is introduced. Knowledge representation system table is taken as a major tool to reduce the rules of expert system in which unnecessary properties are eliminated. The redundancy of fault diagnosis information is revealed. The complexity of fault diagnosis expert system's structure is also reduced. The decision making rules are given finally.     

Decision Model for Location Selection of the Resident Project

Started with studying the evaluation standard, which are influencing the decision for location selection of the resident project, this paper set up a decision model for the location selection. It confirmeds evaluation rule of the location selection according to the characteristic of the resident project and selects all the location by fuzzy mathematic and AHP. After this selection, AHP is used again to taxis all the suitable location to build in the most suitable place. This model is provided with a scientific and rational decision result in the location selection for companies of the real estate.     

Knowledge discovery model of basic oxygen furance steelmaking based on Rough Set Theory

The characteristics of the knowledge discovery for basic oxygen furnace(BOF) steelmaking are analyzed by using the Rough Set Theory. The production data of BOF steelmaking are preprocessed by using the methods of data withdrawal, standardization, discretization and so on. The main influencing factors of steelmaking production are set as the knowledge discovery property. The endpoint control objectives of BOF steelmaking are used as the decision attribute of knowledge discovery. Then the knowledge discovery model of BOF steelmaking based on rough set theory is established, which makes the automation of the production knowledge discovery, access and rule extraction come true. The model is tested by using the production data of 210 t BOF, and takes the temperature variation of smelting endpoint as the decision attribute. The results show that the influencing factors, such as silicon content, iron ore weight, oxygen consumption and so on, are of very importance to the endpoint temperature of molten steel. Besides, the rules of molten steel temperature extracted by the model vary with current converter steelmaking process, which proves the validity of the model.     

Fuzzy c-means Cluster Over Gentic Algorithm

Fuzzy c-means (FCM) algorithm is dynamic cluster algorithm whose result often is local optimal decision. There often exists insignificant clustering in the result of Fuzzy c-means algorithm when traditional union, Intersection and inclusion work in fuzzy set. Our research indicates there are no insignificant clustering in the result of Fuzzy c-means algorithm over genetic algorithm and partial optimal solution can be avoided with this algorithm to a certain extent. The coding, select, corresponding crossover and mutation operators are designed. Finally we compared the performance of GFCM and FCM with testing data. Results show that the performance of GFCM is far better than FCM.     

Fuzzy decision for wastewater treatment process selectionbased on gray theory and its application

Decision making of sewage treatment process directly affects the accuracy and efficiency of the process selection. An optimized fuzzy decision method is proposed based on fuzzy decision and contrast weighting method. Moreover, a web based decision making support system for the selection of sewage treatment process is developed with the Apache+PHP+MySQL development environment. The system had realized the online decision making for sewage treatment process, and enhanced accuracy and reduced the decision making time.     

Organically Combinatorial Inference and Optimization in Intelligence System

The inference algorithm is the most important part in intelligence system because the level of intelligence in the system is decided by it. By means of the mutual benefit for inference algorithm of expert system ,fuzzy logic and neural network ,the combinatorial inference technology which is organically composed of these three parts is put forward for inference mechanism in intelligence system. The optimization decision model is also set up. In order to bring all the advantage of every inference algorithm into play and overcome the disadvantage of single inference algorithm the common expert knowledge base is applied to organic combination and concurrent operation of all inference algorithm. In order to realize knowledge share the inference results are optimized by decision technology optimization. The research results show that the organized combinatorial inference and optimization can be applied in engineering practice effectively and is benefit for raising the inference level.     

Fuzzy Transportation Problem and Its Hybrid Intelligent Algorithm

The authors investigate a more practical transportation problem under fuzzy environment, that is , capacities of supplies and demands in the transportation problem are fuzzy variables. To obtain a directive decision, the authors construct a mathematical model for the fuzzy transportation problem based chance constrained programming and dependent chance programming in fuzzy environment. In addition, since there are many complex fuzzy variables in the mathematical model, the authors design the genetic algorithm to solve the model based on fuzzy simulation. Finally, they give a numerical example to show the efficiency of the algorithm.     

Structure of Closed Sets in Fuzzy Matroid

The authors study the properties and structures of fuzzy closed sets and fuzzy hyperplanes.The relationship between the closed set and it's derived matroid's closed set;The intersection of all hyperplanes in matroids is the smallest closed set and its rank is zero;The intersection of all fuzzy hyperplanes in fuzzy matroids is the smallest closed set and its rank is zero;The characteristics of structures of fuzzy closed sets is showed in using mathematical induction;a necessary and sufficient condition has been found in judging whether a fuzzy set is closed or not.     

Characteristics of the Closure Operator of Fuzzy Matroids

We have discussed the structure of fuzzy matroids.The concept of fuzzy dependence of fuzzy matroids is provided through the generization of element and set in matroid theory,the properties and the conditions of fuzzy dependence are given.Then some properties of fuzzy dependences are studied.A fuzzy_closure axiom of fuzzy matroids is given,which will be significant to further study of matroid's inbeing and to perfection of matroid theory.     

Artificial Neural Network Fault Diagnosis System Based on Rough Set Theory

On the basis of fault diagnosis neural network model, in this paper, knowledge representation system of rough set theory is taken as a major tool to delaminate the complex neural network and in which unnecessary properties are eliminated. This method overcomes some shortcomings, such as network scale is too large and the rate of classification is slow. The good effect that reduces the matching quantity of pattern search in classification course is gotten. The structure and algorithm of layered-mining neural network model based on rough set theory are also given. The example shows that this system has higher reference value in practical application.     

A MULTI -OBJECTIVE FUZZY DECISION -MAKING APPROACH FOR POWER NETWORK PLANNING UNDER UNCERTAINTY

This paper proposes a multi - objective fuzzy decision - making approach for power network planning under uncertainty ,which simutaneously considers not only the least investment cost,the minimum power loss,the maximum reliability,and the least environmental impacts, but also the uncertainty about the future load growth and the capital investment availability. The validity and effectiveness of the proposed approach is verified with numerical examples.     

Design fuzzy membership functions based on the posterior probability weighting

The determination of fuzzy membership function in the fuzzy support vector machine (FSVM) is a difficult problem. To solve the problem of being sensitive to the noises and outliers in support vector machine, by the inspiration of Bayesian decision theory, combining with sample density characteristics, sample points relation between same class and other class is researched, and the tightness on each sample points is described. Based on that, method of posterior probability and sample density weight are given to each sample, and new fuzzy membership function is proposed. The detection of the noises and outliers is avoided by this method. Numerical simulation shows that the improved fuzzy membership function method is effective.     

Model of Multiple Fuzzy Synthetical Evaluation for Enterprise Technology Innovation

Based on analyzing factor of the effect of technology innovation in enterprise,the distinctive multiple technology innovation system is established. The computing method of index weight is discussed in the face of multiple objective decision making, and the authors construct the quantitative analysis model of multiple fuzzy evaluation through fuzzy transform. With the present situation of certain manufacturing enterprise, some suggestions are given in the end after quantitative analyzing.     

The Fuzzy Inputing And Outputing Method in Vibration Fault Diagnosis of Steam Turbine-Generator Set

A fuzzy neural network diagnosis model is established on the basis of the vibration failure features of steam turbine-gernerator set, two kinds of fuzzy inputing method are discussed. At last, the performance of the fuzzy neural network is compared with that of the conventional BP network. The results show that the method presented is suitable for identifying the vibration failure of steam turbine-gernerator set, and it is more efficient in deal with the uncertain data than BP network diagnosis.     

Fuzzy Control and Simulation of Regulating System for Permanent Magnet Synchronous Motor

This paper researches the fuzzy control and simulation of regulating system for PMSM in electrical vehicle. The mathematics model is set up.The PMSM,the PMSM measures demux, dq to abc converter and the three phrase PWM converter models are set up in MATLAB/ Simulink. The fuzzy controller is designed with two dimension fuzzy design method, and take E and EC as input variable, U is the output variable. According to the controller and models, the fuzzy control model of regulating system for PMSM is created in matlab/simulink. At last, to test the accuracy of the control method with the simulation result.     

The application of rough sets-neural network theory to mine ventilation system evaluation

To solve the instability problem of established sample in the neural network evaluation method for mine ventilation system, a comprehensive evaluation of the ventilation system is carried out based on rough sets and BP neural networks. Taking the ventilation system of a mine as an example, the classification quality of raw data samples are tested by using rough set data analysis system. Then, based on artificial neural network theory, a rough sets-neural network evaluation model of a mine ventilation system is established and a new rough sets-neural network evaluation method of mine ventilation system is formed. The results show that, after the model validation of data and application, its theoretical evaluation results are in line with the actual situation, and the network total error is less than 0.004. It shows that the comprehensive evaluation method based on rough sets-neural networks has a good effect in evaluating mine ventilation system in practical application.     

Fuzzy Backpropagation Algorithms and Their Convergence

The equivalence between fuzzy neural networks model for max-min fuzzy operator and S.Stoeva's is proved by studying the fuzzy neural networks model for max-min fuzzy based on S.Stoeva's.Then the paper proposes the fuzzy backpropagation learning algorithms for changing fuzzy power and probes their convergence properties.Finally,it simulates experiment such as state monitoring of turbo-generator set.The results show that the fuzzy backpropagation learning algorithms presented are convergent on condition that the output of training sample is between maximum and minimum of its input.     

Determination to Weighting Coefficient of Combination Forecast Based on Rough Set Theory

The problem of determination to weighting coefficient is a key and difficulty for combination forecast. A method of determining weighting coefficient based on rough set theory is showed in this paper. Determining weighting coefficient is translated into estimating significance of attributes among rough set. A relation data model about combination forecast is established. Knowledge systems are built through making attribute value into eigenvalue. Under data moving, the weighting coefficients of a combination forecast model are computed by analyzing the dependence and significance of forecasting method for the predicted object. The proposed approach overcomes the subjectivity of traditional determination to weighting coefficient, avoids computing linear or nonlinear extremum problem and makes combination forecast more objective. The validity of the proposed approach is verified with a case.