Comprehensive Evaluation of New Maize Varieties for Grain and Fodder Based on Membership Function Method

To screen new maize(Zea mays L.)varieties suitable for food and fodder dual-purpose in Du'an Yao Autonomous County of Guangxi,the agronomic characters,yield and quality indexes of 12 new maize varieties were measured,and the correlation between various indexes were analyzed,and the comprehensive performance of tested varieties was evaluated by membership function method.The results showed that Guidan 671 had the highest grain yield and whole-plant biomass at 10908 and 49965 kg/hm^(2),respectively,and the second was Zhaoyu 215 with a grain yield and whole-plant biomass of 10086 and 47175 kg/hm^(2),respectively.Grain yield was highly significantly positively correlated with ear diameter and 100-grain weight(P<0.01),and significantly correlated with whole-plant biomass,starch content,ear length and grain number per row(P<0.05);and the whole-plant biomass was highly significantly correlated with the number of grains per row(P<0.01),and significantly correlated with starch content,panicle length,plant height and panicle height(P<0.05).The comprehensive performance scores of the tested varieties from high to low were Guidan 671,Zhaoyu 215,Guidan 669,Guidan 6208,Guidan 666,Guidan 6205,Guidan 660,Guidan 6203,Guidan 6206,Guidan 162,Guidan 668 and Guidan 673.According to the values of membership function and combined with various indexes,Guidan 671 and Zhaoyu 215 had good comprehensive performance,and could be used as the first choice for food and fodder dual-purpose maize varieties in Du'an Yao Autonomous County.

Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

In this article,the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering.First,an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule.Then,compared to traditional prediction-based ones,two types of fuzzy set-membership filters are proposed to effectively improve filtering performance,where the structure of both filters consists of two parts:prediction and filtering.Under the locally Lipschitz continuous condition of membership functions,unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error.Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state.Finally,the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.

基于Membership的高可用性Linux集群

介绍了Ｌｉｎｕｘ集群高可用性（ＨＡ）技术的应用背景和体系结构，重点讲述了集群Ｍｅｍｂｅｒｓｈｉｐ技术可以显著提高集群系统的可用性，并且提出了可配置的Ｍｅｍｂｅｒｓｈｉｐ服务（ＭＢＬＣ）来实现Ｌｉｎｕｘ集群的高可用性的方案。

基于Membership拓展的权限机制设计

给出一种基于ASP.NET中的Membership拓展与传统的RBAC机制相结合形成的一种权限机制,它能够实现在Web页面中对于角色与权限的有效控制访问,并且改进了Membership的对权限控制力度不够的缺陷以及传统RBAC机制不能有效利用ASP.NET控件的劣势。最后,在"研究生信息管理系统"项目中实现这种权限机制,充分验证了其优势以及可行性和有效性。

Fuzzy logic controller design with unevenly-distributed membership function for high performance chamber cooling system

Fuzzy logic controller adopting unevenly-distributed membership function was presented with the purpose of enhancing performance of the temperature control precision and robustness for the chamber cooling system.Histogram equalization and noise detection were performed to modify the evenly-distributed membership functions of error and error change rate into unevenly-distributed membership functions.Then,the experimental results with evenly and unevenly distributed membership functions were compared under the same outside environment conditions.The experimental results show that the steady-state error is reduced around 40% and the noise disturbance is rejected successfully even though noise range is 60% of the control precision range.The control precision is improved by reducing the steady-state error and the robustness is enhanced by rejecting noise disturbance through the fuzzy logic controller with unevenly-distributed membership function.Moreover,the system energy efficiency and lifetime of electronic expansion valve(EEV) installed in chamber cooling system are improved by adopting the unevenly-distributed membership function.

Ellipsoidal bounding set-membership identification approach for robust fault diagnosis with application to mobile robots

A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation (FDI).

New Methods of Fitting the Membership Function of Oceanic Water Masses

After reviewing the analytical theories of T S curve, some methods of T S relationship, and fuzzy sets for studying water masses, new methods of fitting the membership function of oceanic water masses are presented based on the characteristics of T S curve family of oceanic water masses. The membership functions of oceanic Subsurface Water Mass with high salinity and Intermediate Water Mass with low salinity are fitted and discussed using the new methods. The proposed methods are useful in analyzing the mixing and modifying processes of these water masses, especially in tracing their sources. The principles and formulae of the new methods and examples are given.

Feature Selection Based on Adaptive Fuzzy Membership Functions

Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some researchers.However,their rule number will grow exponentially as the data dimension increases.On the other hand,feature selection algorithms with artificial neural networks(ANN)usually require normalization of input data,which will probably change some characteristics of original data that are important for classification.To overcome the problems mentioned above,this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron(MLP)to form a new artificial neural network.Furthermore,fuzzification strategy and feature measurement based on membership space are proposed for feature selection. Finally,experiments with both natural and artificial data are carried out to compare with other methods,and the results approve the validity of the algorithm.

Rough function model and rough membership function

Two pairs of approximation operators, which are the scale lower and upper approximations as well as the real line lower and upper approximations, are defined. Their properties and antithesis characteristics are analyzed. The rough function model is generalized based on rough set theory, and the scheme of rough function theory is made more distinct and complete. Therefore, the transformation of the real function analysis from real line to scale is achieved. A series of basic concepts in rough function model including rough numbers, rough intervals, and rough membership functions are defined in the new scheme of the rough function model. Operating properties of rough intervals similar to rough sets are obtained. The relationship of rough inclusion and rough equality of rough intervals is defined by two kinds of tools, known as the lower （upper） approximation operator in real numbers domain and rough membership functions. Their relative properties are analyzed and proved strictly, which provides necessary theoretical foundation and technical support for the further discussion of properties and practical application of the rough function model.

Multi-attribute decision making model based on optimal membership and relative entropy

To study the problems of multi-attribute decision making in which the attribute values are given in the form of linguistic fuzzy numbers and the information of attribute weights are incomplete, a new multi-attribute decision making model is presented based on the optimal membership and the relative entropy. Firstly, the definitions of the optimal membership and the relative entropy are given. Secondly, for all alternatives, a set of preference weight vectors are obtained by solving a set of linear programming models whose goals axe all to maximize the optimal membership. Thirdly, a relative entropy model is established to aggregate the preference weight vectors, thus an optimal weight vector is determined. Based on this optimal weight vector, the algorithm of deviation degree minimization is proposed to rank all the alternatives. Finally, a decision making example is given to demonstrate the feasibility and rationality of this new model.

Evaluation of Membership Degree on Conventional Chemical Components in Tobacco from Tobacco Leaf Bases in Yunnan Province Based on Cigarette Brands

[Objective] This study was conducted to investigate the quality condition of chemical components of tobacco in Yunnan. [Method] The C3F samples were collected from 76 base units in Yunnan Province and their conventional chemical components were analyzed, and the evaluation was carded out based on the membership function and hierarchical cluster analysis of cigarette brands H1 and H2 in Yunnan. [Result] The results showed that： （1） Major chemical components of 76 base units were coordinated overall. （2） Total nitrogen of brand H1 was higher than that of the high-quality tobacco leaves by 12.08%; raw tobacco of brands H3 and H2 satisfied the quality requirements of high-quality tobacco in Yunnan; and the total sugar and reducing sugar in flue-cured tobacco of brand H4 were higher those of the high-quality tobacco leaves by 0.76% and 10.3%, respectively. （3） The total sugar, reducing sugar and potassium of tobacco leaves from bases of G1 group were higher than those of tobacco from bases of G2 group by 38.1%, 2.27% and 7.34%, respectively; and total nitrogen and chlorine were lower by 4.69% and 11.11%, respectively; and nicotine contents in tobacco of the two groups were similar. （4） H2 and H3 were not significantly different in main chemical components; H3 and H4 were significantly different in total nitrogen, while other main chemical components were not significant different; and there were no significant differences between H4 and H2. [Conclusion] The quality of tobacco leaves from tobacco base units of G1 and G2 groups was better. Therefore, the evaluation provides theoretical reference for construction of tobacco base units.

Rock burst laws in deep mines based on combined model of membership function and dominance-based rough set

Rock bursts are spontaneous, violent fracture of rock that can occur in deep mines, and the likelihood of rock bursts occurring increases as depth of the mine increases. Rock bursts are also affected by the compressive strength, tensile strength, tangential strength, elastic energy index, etc. of rock, and the relationship between these factors and rock bursts in deep mines is difficult to analyze from quantitative point. Typical rock burst instances as a sample set were collected, and membership function was introduced to process the discrete values of these factors with the discrete factors as condition attributes and rock burst situations as decision attributes. Dominance-based rough set theory was used to generate preference rules of rock burst, and eventually rock burst laws analysis in deep mines with preference relation was taken. The results show that this model for rock burst laws analysis in deep mines is more reasonable and feasible, and the prediction results are more scientific.

Membership Proof and Verification in Authenticated Skip Lists Based on Heap

How to keep cloud data intact and available to users is a problem to be solved. Authenticated skip list is an important data structure used in cloud data integrity verification. How to get the membership proof of the element in authenticated skip list efficiently is an important part of authentication. Kaouthar Blibech and Alban Gabillon proposed a head proof and a tail proof algorithms for the membership proof of elements in the authenticated skip list. However, the proposed algorithms are uncorrelated each other and need plateau function. We propose a new algorithm for computing the membership proof for elements in the authenticated skip list by using two stacks, one is for storing traversal chain of leaf node, the other is for storing authentication path for the leaf. The proposed algorithm is simple and effective without needing plateau function. It can also be applicable for other similar binary hash trees.

On Income Distribution Mechanism of Farmer Cooperatives in the Context of Membership Heterogeneity

With reference to several possible solutions to the issue of two subject allocation,using the Accumulation Point analysis method in Game Theory,this paper analyzed the income distribution mechanism between large farmers and small farmers in farmer cooperatives in the context of membership heterogeneity. It found that,in the practice of the income distribution in farmer cooperatives,there possibly exists equalization solution,pure utility solution,Nash solution and Kalai-Smorodinsky solution and it will be affected by social conventions. Finally,it made an empirical analysis using five cases of farmer cooperatives.

Life-cycle cost model and evaluation system for power grid assets based on fuzzy membership degree

Life-cycle cost(LCC)theory can be effectively applied to improve the efficiency and quality of power plant equipment and asset management.However,specific aspects of the LCC calculation and evaluation model require further research for practical application.This paper proposes an LCC assessment model for the management of electric power plant equipment during its service life.A membership function method based on fuzzy logic is used to improve the allocation of modernization and overhaul projects to multiple equipment assets.An LCC assessment model and evaluation system for power equipment are proposed and successfully applied to the equipment and project management of a Guangzhou power plant in the China Southern Power Grid,providing a decision-making mechanism that facilitates efficient operation and optimal utilization of power plant equipment and assets.

An Approach to Formulation of FNLP with Complex Piecewise Linear Membership Functions

Traditionally, extra binary variables are demanded to formulate a fuzzy nonlinear programming(FNLP) problem with piecewise linear membership functions(PLMFs). However, this kind of methodology usually suffers increasing computational burden associated with formulation and solution, particularly in the face of complex PLMFs. Motivated by these challenges, this contribution introduces a novel approach free of additional binary variables to formulate FNLP with complex PLMFs, leading to superior performance in reducing computational complexity as well as simplifying formulation. A depth discussion about the approach is conducted in this paper, along with a numerical case study to demonstrate its potential benefits.

The Theory of Membership Degree of Γ-Conclusion in Several n-Valued Logic Systems

Based on the analysis of the properties of Γ-conclusion by means of deduction theorems, completeness theorems and the theory of truth degree of formulas, the present papers introduces the concept of the membership degree of formulas A is a consequence of Γ (or Γ-conclusion) in Lukasiewicz n-valued propositional logic systems, Godel n-valued propositional logic system and the R0 n-valued propositional logic systems. The condition and related calculations of formulas A being Γ-conclusion were discussed by extent method. At the same time, some properties of membership degree of formulas A is a Γ-conclusion were given. We provide its algorithm of the membership degree of formulas A is a Γ-conclusion by the constructions of theory root.

Incremental learning of the triangular membership functions based on single-pass FCM and CHC genetic model

In order to improve the efficiency of learning the triangular membership functions( TMFs) for mining fuzzy association rule( FAR) in dynamic database,a single-pass fuzzy c means( SPFCM)algorithm is combined with the real-coded CHC genetic model to incrementally learn the TMFs. The cluster centers resulting from SPFCM are regarded as the midpoint of TMFs. The population of CHC is generated randomly according to the cluster center and constraint conditions among TMFs. Then a new population for incremental learning is composed of the excellent chromosomes stored in the first genetic process and the chromosomes generated based on the cluster center adjusted by SPFCM. The experiments on real datasets show that the number of generations converging to the solution of the proposed approach is less than that of the existing batch learning approach. The quality of TMFs generated by the approach is comparable to that of the batch learning approach. Compared with the existing incremental learning strategy,the proposed approach is superior in terms of the quality of TMFs and time cost.

On Development of Fuzzy Controller: The Case of Gaussian and Triangular Membership Functions

In recent years, the use of Fuzzy set theory has been popularised for handling overlap domains in control engineering but this has mostly been within the context of triangular membership functions. In actual practice however, such domains are hardly triangular and in fact for most engineering applications the membership functions are usually Gaussian and sometimes cosine. In an earlier paper, we derived explicit Fourier series expressions for systematic and dynamic computation of grade of membership in the overlap and non-overlap regions of triangular Fuzzy sets. In another paper, we extended the methodology to cover cases of cosine, exponential and Gaussian Fuzzy sets by presenting explicit Fourier series representation for encoding fuzziness in the overlap and non-overlap domains of Fuzzy sets. This current paper presents the development of a “Fuzzy Controller” device, which incorporates the formal mathematical representation for computing grade of membership of Gaussian and triangular Fuzzy sets. It is shown that triangular approximation of Gaussian membership function in Fuzzy control can lead to wrong linguistic classification which may have adverse effects on operational and control decisions. The development of the Fuzzy controller demonstrates that the proposed technique can indeed be incorporated in engineering systems for dynamic and systematic computation of grade of membership in the overlap and non-overlap regions of Fuzzy sets;and thus provides a basis for the design of embedded Fuzzy controller for mission critical applications.

INVESTIGATION ON MAKING MEMBERSHIP FUNCTION OF WATER MASS FROM MIXING MODIFIED PROCESS

A water mass in the sea area under investigation is defined as a fuzzy subset in the discourse universe. Possible forms of membership function of water masses in the mixing modified process are discussed with the mixing theory for conservative concentration of sea water. It may provide bases for making membership functions. Results in this paper may be extended and applied to shallow water. Examples and discussion are given in this paper.