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Fuzzy inference systems with no any rule base and linearly parameter growth

控制理论与应用（英文版） 2004年第2期2卷 185-192页

作者： ShitongWANC KorrisF.L.CHUNG JiepingLU BinHAN DewenHU DepartmentofComputer SchoolofInformationSouthernYangtseUniversityWuxiJiangsu214036China DeparmentofComputing HongKongPolytechnicUniversityHongKongChina DepartmentofComputer SoutheastUniversityNanjingJiangsu210016China DepartrmentofComputer EastChinaShipbuildingInstituteZhenjiangJiangsu212003China SchoolofAutomation NationalDefenseUniversityofScience＆TechnologyChangshaHunan410073China

A class of new fuzzy inference systems New-FISs is *** with the standard fuzzy system, New-FIS is still a universal approximator and has no fuzzy rule base and linearly parameter growth. Thus, it effectively overcomes the second "curse of dimensionality":there is an exponential growth in the number of parameters of a fuzzy system as the number of input variables,resulting in surprisingly reduced computational complexity and being especially suitable for applications,where the complexity is of the first importance with respect to the approximation accuracy.

关键词： Fuzzy inference Fuzzy systems Universal approximation Computational complexity Linearly parameter growth

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Efficient Location-Aware Data Placement for Data-Intensive Applications in Geo-distributed Scientific Data Centers

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Tsinghua Science and technology 2016年第5期21卷 471-481页

作者： Jinghui Zhang Jian Chen Junzhou Luo Aibo Song School of Computer Science and Engineering Southeast University Nanjing 211189 China

Recent developments in cloud computing and big data have spurred the emergence of data-intensive applications for which massive scientific datasets are stored in globally distributed scientific data centers that have a high frequency of data access by scientists worldwide. Multiple associated data items distributed in different scientific data centers may be requested for one data processing task, and data placement decisions must respect the storage capacity limits of the scientific data centers. Therefore, the optimization of data access cost in the placement of data items in globally distributed scientific data centers has become an increasingly important *** data placement approaches for geo-distributed data items are insufficient because they either cannot cope with the cost incurred by the associated data access, or they overlook storage capacity limitations, which are a very practical constraint of scientific data centers. In this paper, inspired by applications in the field of high energy physics, we propose an integer-programming-based data placement model that addresses the above challenges as a Non-deterministic Polynomial-time（NP）-hard problem. In addition we use a Lagrangian relaxation based heuristics algorithm to obtain ideal data placement solutions. Our simulation results demonstrate that our algorithm is effective and significantly reduces overall data access cost.

关键词： data placement geo-distributed data center Lagrangian relaxation

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