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The Proof of Inseparable Prime AW^＊—Algebras Being Primitive C^＊—Algebras

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Acta Mathematica Sinica,English Series 2003年第2期19卷 413-416页

作者： LunChuanZHANG schoolofinformationscience RenminUniversityBeijing100091P.R.China

The relation between the inseparable prime C*-algebras and primitive C*-algebras is studied, and we prove that prime AW*-algebras are all primitive C*-algebras.

关键词： Prime C* algebras Primitive C* algebras AW* algebras Hereditary C* subalgebras

Chaotic migration-based pseudo parallel genetic algorithm and its application in inventory optimization

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Journal of Systems Engineering and Electronics 2005年第2期16卷 411-417页

作者： ChenXiaofang GuiWeihua WangYalin schoolofinformationscience&Engineering CentralSouthUniversityChangsha410083P.R.China

Considering premature convergence in the searching process of genetic algorithm, a chaotic migration-based pseudo parallel genetic algorithm (CMPPGA) is proposed, which applies the idea of isolated evolution and information exchanging in distributed Parallel Genetic Algorithm by serial program structure to solve optimization problem of low real-time demand. In this algorithm, asynchronic migration of individuals during parallel evolution is guided by a chaotic migration sequence. Information exchanging among sub-populations is ensured to be efficient and sufficient due to that the sequence is ergodic and stochastic. Simulation study of CMPPGA shows its strong global search ability, superiority to standard genetic algorithm and high immunity against premature convergence. According to the practice of raw material supply, an inventory programming model is set up and solved by CMPPGA with satisfactory results returned.

关键词： parallel genetic algorithm chaos premature convergence inventory optimization.

Constructing Projection Frequent Pattern Tree for Efficient Mining

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Wuhan University Journal of Natural Sciences 2003年第02A期8卷 351-357页

作者： XiangJian-wen HeYan-xiang KokichiFutatsugi KongWei-qiang SchoolofComputer WuhanUniversityWuhan430072HubeiChina StateKeyLabofSoftwareEngineering WuhanUniversityWuhan430072HubeiChina schoolofinformationscience JapanAdvancedInstituteofScienceandTechnology1-1AsahidaiTatsunokuchiIshikawa923-1292Japan

Frequent Pattern mining plays an essential role in data mining. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns. We introduce a novel frequent pattern growth (FP-growth) method, which is efficient and scalable for mining both long and short frequent patterns without candidate generation. And build a new projection frequent pattern tree (PFP-tree) algorithm, which not only heirs all the advantages in the FP-growth method, but also avoids it's bottleneck in database size dependence when constructing the frequent pattern tree (FP-tree). Efficiency of mining is achieved by introducing the projection technique, which avoid serial scan each frequent item in the database, the cost is mainly related to the depth of the tree, namely the number of frequent items of the longest transaction in the database, not the sum of all the frequent items in the database, which hugely shortens the time of tree-construction. Our performance study shows that the PFP-tree method is efficient and scalable for mining large databases or data warehouses, and is even about an order of magnitude faster than the FP-growth method.

关键词： Algorithms Database systems Trees (mathematics)

Dissipative-based adaptive neural control for nonlinear systems

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控制理论与应用（英文版） 2004年第2期2卷 126-130页

作者： YugangNIU XingyuWANG JunweiLU schoolofinformationscience＆Engineering EastChinaUniversityofScience＆TechnologyShanghai200237China CollegeofElectricalandElectronicEngineering NanjingNormalUniversityNanjingJiangsu210042China

A dissipative-based adaptive neural control scheme was developed for a class of nonlinear uncertain systems with unknown nonlinearities that might not be linearly parameterized. The major advantage of the present work was to relax the requirement of matching condition, i.e., the unknown nonlinearities appear on the same equation as the control input in a state-space representation, which was required in most of the available neural network controllers. By synthesizing a state-feedback neural controller to make the closed-loop system dissipative with respect to a quadratic supply rate, the developed control scheme guarantees that the L2-gain of controlled system was less than or equal to a prescribed level. And then, it is shown that the output tracking error is uniformly ultimate bounded. The design scheme is illustrated using a numerical simulation.

关键词： Nonlinear systems Adaptive control Dissipative theory Neural networks

UNDERSTANDING OF FUZZY OPTIMIZATION:THEORIES AND METHODS

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Journal of Systems Science & Complexity 2004年第1期17卷 117-136页

作者： TANGJiafu WANGDingwei Richard YKFUNG Kai-LeungYung DepartmentofSystemsEngineering SchoolofInformationScience&EngineeringNortheasternUniversityShenyang110004China DepartmentofManufacturingEngineering&EngineeringManagement CityUniversityofHongKongTatCheeAvenueKowloonHongKongChina DepartmentofIndustrialandSystemsEngineering TheHongKongPolytechnicUniversityHungHomKowLoonHongKongChina

A brief summary on and comprehensive understanding of fuzzy optimizationis presented. This summary is made on aspects of fuzzy modelling and fuzzy optimization,classification and formulation for the fuzzy optimization problems, models and *** importance of interpretation of the problem and formulation of the optimal solutionin fuzzy sense are emphasized in the summary of the fuzzy optimization.

关键词： fuzzy optimization theory and methods survey