Solving complex problems by multi-agent systems in distributed environments requires new approximate reasoning methods based on new computing paradigms. One such recently emerging computing paradigm is Granular Comput...Solving complex problems by multi-agent systems in distributed environments requires new approximate reasoning methods based on new computing paradigms. One such recently emerging computing paradigm is Granular Computing(GC). We discuss the Rough-Granular Computing(RGC) approach to modeling of computations in complex adaptive systems and multiagent systems as well as for approximate reasoning about the behavior of such systems. The RGC methods have been successfully applied for solving complex problems in areas such as identification of objects or behavioral patterns by autonomous systems, web mining, and sensor fusion.展开更多
Computer aided process planning（CAPP） is an important content of computer integrated manufacturing, and intelligentizing is the orientation of development of CAPP. Process planning has characters of empirical and ti...Computer aided process planning（CAPP） is an important content of computer integrated manufacturing, and intelligentizing is the orientation of development of CAPP. Process planning has characters of empirical and time-consuming to finalize, and the same technical aim always can be achieved by different process schemes, so intelligentizing of process decision making always be a difficult point of CAPP and computer integrated manufacturing （CIM）. For the purpose of intelligent aided process decision making and reuse of process resource, this paper proposed a decision making method based on rough sets（RS） and regular distance computing. The main contents and methods of process planning decision making are analyzed under agile response manufacturing environment, the concept of process knowledge granule is represented, and the methods of process knowledge granule partitioning and granularity analysis are put forward. Based on the theory of RS and combined the method of process attributes importance identification, the paper brought forward a computing model for process scheme regulation distance under the same attribute conditions, and conflict resolution strategy was introduced to acquire process scheme fit for actual situation of enterprise＇s manufacturing resources, so as to realize process resources＇ conflict resolution and quick excavate and reuse of enterprises＇ existing process knowledge, to advance measures of process decision making and improve the rationality and capability of agile response of process planning.展开更多
Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions.DRSA has also some merits within granular computin...Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions.DRSA has also some merits within granular computing,as it extends the paradigm of granular computing to ordered data,specifies a syntax and modality of information granules which are appropriate for dealing with ordered data,and enables computing with words and reasoning about ordered data.Granular computing with ordered data is a very general paradigm,because other modalities of information constraints,such as veristic,possibilistic and probabilistic modalities,have also to deal with ordered value sets(with qualifiers relative to grades of truth,possibility and probability),which gives DRSA a large area of applications.展开更多
The rapid expansion of the Internet has resulted not only in the ever growing amount of data therein stored,but also in the burgeoning complexity of the concepts and phenomena pertaining to those data.This issue has b...The rapid expansion of the Internet has resulted not only in the ever growing amount of data therein stored,but also in the burgeoning complexity of the concepts and phenomena pertaining to those data.This issue has been vividly compared by the renowned statistician,prof.Friedman of Stanford University,to the advances in human mobility from the period of walking afoot to the era of jet travel.These essential changes in data have brought new challenges to the development of new data mining methods,especially that the treatment of these data increasingly involves complex processes that elude classic modeling paradigms."Hot" datasets like biomedical,financial or net user behavior data are just a few examples.Mining such temporal or stream data is on the agenda of many research centers and companies worldwide.In the data mining community,there is a rapidly growing interest in developing methods for process mining,e.g.,for discovery of structures of temporal processes from data.Works on process mining have recently been undertaken by many renowned centers worldwide.This research is also related to functional data analysis,cognitive networks,and dynamical system modeling,e.g.,in biology.In the lecture,we outline an approach to discovery of processes from data and domain knowledge which is based on the rough-granular computing.展开更多
Rough set philosophy hinges on the granularity of data, which is used to build all its basic concepts, like approximations, dependencies, reduction etc. Genetic Algorithms provides a general frame to optimize problem ...Rough set philosophy hinges on the granularity of data, which is used to build all its basic concepts, like approximations, dependencies, reduction etc. Genetic Algorithms provides a general frame to optimize problem solution of complex system without depending on the domain of problem.It is robust to many kinds of problems.The paper combines Genetic Algorithms and rough sets theory to compute granular of knowledge through an example of information table. The combination enable us to compute granular of knowledge effectively.It is also useful for computer auto-computing and information processing.展开更多
In this paper, we conduct research on the development trend and general applications of the fuzzy rough granular computing theory. Granular computing is a new concept of general information processing and computing pa...In this paper, we conduct research on the development trend and general applications of the fuzzy rough granular computing theory. Granular computing is a new concept of general information processing and computing paradigm which covers all the granularity the study of the theory, methods, techniques and the tools. In many areas are the basic ideas of granular computing, such as the interval analysis, rough set theory, clustering analysis and information retrieval, machine learning, database, etc. With the theory of domain known division of target concept and rule acquisition, in knowledge discovery, data mining and the pattern recognition is widely used. Under this basis, in this paper, we propose the fuzzy rough theory based computing paradigm that gains ideal performance.展开更多
In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between ...In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data.We advocate that the level of abstraction,which can be flexibly adjusted,is conveniently realized through Granular Computing.Granular Computing is concerned with the development and processing information granules–formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there.This study identifies the principles of Granular Computing,shows how information granules are constructed and subsequently used in describing relationships present among the data.展开更多
Granular Computing on partitions(RST),coverings(GrCC) and neighborhood systems(LNS) are examined: (1) The order of generality is RST, GrCC, and then LNS. (2) The quotient structure: In RST, it is called quotient set. ...Granular Computing on partitions(RST),coverings(GrCC) and neighborhood systems(LNS) are examined: (1) The order of generality is RST, GrCC, and then LNS. (2) The quotient structure: In RST, it is called quotient set. In GrCC, it is a simplical complex, called the nerve of the covering in combinatorial topology. For LNS, the structure has no known description. (3) The approximation space of RST is a topological space generated by a partition, called a clopen space. For LNS, it is a generalized/pretopological space which is more general than topological space. For GrCC,there are two possibilities. One is a special case of LNS,which is the topological space generated by the covering. There is another topological space, the topology generated by the finite intersections of the members of a covering The first one treats covering as a base, the second one as a subbase. (4) Knowledge representations in RST are symbol-valued systems. In GrCC, they are expression-valued systems. In LNS, they are multivalued system; reported in 1998 . (5) RST and GRCC representation theories are complete in the sense that granular models can be recaptured fully from the knowledge representations.展开更多
This paper reviews a class of important models of granular computing which are induced by equivalence relations,or by general binary relations,or by neighborhood systems,and propose a class of models of granular compu...This paper reviews a class of important models of granular computing which are induced by equivalence relations,or by general binary relations,or by neighborhood systems,and propose a class of models of granular computing which are induced by coverings of the given universe.展开更多
In granular computing granular structures represent knowledge on universe,in this paper several important granular structures are considered.In a general granular structure the notions of interior point, accumulation ...In granular computing granular structures represent knowledge on universe,in this paper several important granular structures are considered.In a general granular structure the notions of interior point, accumulation point and boundary point etc are proposed,by use of these notions and referring to topological method,the lower and upper approximations of a subset of universe are defined such that they are one kind of generalization of the existing approximations based on some special granular structure.Basic properties of new rough set approximations are investigated.Furthermore,granular structures on universe are characterized by the lower and upper approximation operators.展开更多
With the rapid development of the cloud computing technology, it has matured enough for a lot of individuals and organizations to move their work into the cloud. Correspondingly, a variety of cloud services are emergi...With the rapid development of the cloud computing technology, it has matured enough for a lot of individuals and organizations to move their work into the cloud. Correspondingly, a variety of cloud services are emerging. It is a key issue to assess the cloud services in order to help the cloud users select the most suitable cloud service and the cloud providers offer this service with the highest quality. The criteria parameters defining the cloud services are complex which lead to cloud service deviation. In this paper, we propose an assessment method of parameters importance in cloud services using rough set theory. The method can effectively compute the importance of cloud services parameters and sort them. On the one hand, the calculation can be used as the credible reference when users choose their appropriate cloud services. On the other hand, it can help cloud service providers to meet user requirements and enhance the user experience. The simulation results show the effectiveness of the method and its relevance in the cloud context.展开更多
The two universes multi-granularity fuzzy rough set model is an effective tool for handling uncertainty problems between two domains with the help of binary fuzzy relations. This article applies the idea of neighborho...The two universes multi-granularity fuzzy rough set model is an effective tool for handling uncertainty problems between two domains with the help of binary fuzzy relations. This article applies the idea of neighborhood rough sets to two universes multi-granularity fuzzy rough sets, and discusses the two-universes multi-granularity neighborhood fuzzy rough set model. Firstly, the upper and lower approximation operators are defined in the two universes multi-granularity neighborhood fuzzy rough set model. Secondly, the properties of the upper and lower approximation operators are discussed. Finally, the properties of the two universes multi-granularity neighborhood fuzzy rough set model are verified through case studies.展开更多
基金The grant3 T11C 00226 from Min istroyf ScientifiRcesearchand InformationTechnologyoftheRepublicofPoland.
文摘Solving complex problems by multi-agent systems in distributed environments requires new approximate reasoning methods based on new computing paradigms. One such recently emerging computing paradigm is Granular Computing(GC). We discuss the Rough-Granular Computing(RGC) approach to modeling of computations in complex adaptive systems and multiagent systems as well as for approximate reasoning about the behavior of such systems. The RGC methods have been successfully applied for solving complex problems in areas such as identification of objects or behavioral patterns by autonomous systems, web mining, and sensor fusion.
基金supported by National Key Technology R&D Program of China (Grant No. 2006BAF01A07)National Hi-tech Research and Development Program of China (863 Program, Grant No. 2007AA04Z190)
文摘Computer aided process planning（CAPP） is an important content of computer integrated manufacturing, and intelligentizing is the orientation of development of CAPP. Process planning has characters of empirical and time-consuming to finalize, and the same technical aim always can be achieved by different process schemes, so intelligentizing of process decision making always be a difficult point of CAPP and computer integrated manufacturing （CIM）. For the purpose of intelligent aided process decision making and reuse of process resource, this paper proposed a decision making method based on rough sets（RS） and regular distance computing. The main contents and methods of process planning decision making are analyzed under agile response manufacturing environment, the concept of process knowledge granule is represented, and the methods of process knowledge granule partitioning and granularity analysis are put forward. Based on the theory of RS and combined the method of process attributes importance identification, the paper brought forward a computing model for process scheme regulation distance under the same attribute conditions, and conflict resolution strategy was introduced to acquire process scheme fit for actual situation of enterprise＇s manufacturing resources, so as to realize process resources＇ conflict resolution and quick excavate and reuse of enterprises＇ existing process knowledge, to advance measures of process decision making and improve the rationality and capability of agile response of process planning.
文摘Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions.DRSA has also some merits within granular computing,as it extends the paradigm of granular computing to ordered data,specifies a syntax and modality of information granules which are appropriate for dealing with ordered data,and enables computing with words and reasoning about ordered data.Granular computing with ordered data is a very general paradigm,because other modalities of information constraints,such as veristic,possibilistic and probabilistic modalities,have also to deal with ordered value sets(with qualifiers relative to grades of truth,possibility and probability),which gives DRSA a large area of applications.
基金supported by the grant N N516 368334 from Ministry of Science and Higher Education of the Republic of Poland and by the grant Innovative Economy Operational Programme 2007-2013(Priority Axis 1.Research and development of new technologies)managed by Ministry of Regional Development of the Republic of Poland.
文摘The rapid expansion of the Internet has resulted not only in the ever growing amount of data therein stored,but also in the burgeoning complexity of the concepts and phenomena pertaining to those data.This issue has been vividly compared by the renowned statistician,prof.Friedman of Stanford University,to the advances in human mobility from the period of walking afoot to the era of jet travel.These essential changes in data have brought new challenges to the development of new data mining methods,especially that the treatment of these data increasingly involves complex processes that elude classic modeling paradigms."Hot" datasets like biomedical,financial or net user behavior data are just a few examples.Mining such temporal or stream data is on the agenda of many research centers and companies worldwide.In the data mining community,there is a rapidly growing interest in developing methods for process mining,e.g.,for discovery of structures of temporal processes from data.Works on process mining have recently been undertaken by many renowned centers worldwide.This research is also related to functional data analysis,cognitive networks,and dynamical system modeling,e.g.,in biology.In the lecture,we outline an approach to discovery of processes from data and domain knowledge which is based on the rough-granular computing.
文摘Rough set philosophy hinges on the granularity of data, which is used to build all its basic concepts, like approximations, dependencies, reduction etc. Genetic Algorithms provides a general frame to optimize problem solution of complex system without depending on the domain of problem.It is robust to many kinds of problems.The paper combines Genetic Algorithms and rough sets theory to compute granular of knowledge through an example of information table. The combination enable us to compute granular of knowledge effectively.It is also useful for computer auto-computing and information processing.
文摘In this paper, we conduct research on the development trend and general applications of the fuzzy rough granular computing theory. Granular computing is a new concept of general information processing and computing paradigm which covers all the granularity the study of the theory, methods, techniques and the tools. In many areas are the basic ideas of granular computing, such as the interval analysis, rough set theory, clustering analysis and information retrieval, machine learning, database, etc. With the theory of domain known division of target concept and rule acquisition, in knowledge discovery, data mining and the pattern recognition is widely used. Under this basis, in this paper, we propose the fuzzy rough theory based computing paradigm that gains ideal performance.
文摘In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data.We advocate that the level of abstraction,which can be flexibly adjusted,is conveniently realized through Granular Computing.Granular Computing is concerned with the development and processing information granules–formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there.This study identifies the principles of Granular Computing,shows how information granules are constructed and subsequently used in describing relationships present among the data.
文摘Granular Computing on partitions(RST),coverings(GrCC) and neighborhood systems(LNS) are examined: (1) The order of generality is RST, GrCC, and then LNS. (2) The quotient structure: In RST, it is called quotient set. In GrCC, it is a simplical complex, called the nerve of the covering in combinatorial topology. For LNS, the structure has no known description. (3) The approximation space of RST is a topological space generated by a partition, called a clopen space. For LNS, it is a generalized/pretopological space which is more general than topological space. For GrCC,there are two possibilities. One is a special case of LNS,which is the topological space generated by the covering. There is another topological space, the topology generated by the finite intersections of the members of a covering The first one treats covering as a base, the second one as a subbase. (4) Knowledge representations in RST are symbol-valued systems. In GrCC, they are expression-valued systems. In LNS, they are multivalued system; reported in 1998 . (5) RST and GRCC representation theories are complete in the sense that granular models can be recaptured fully from the knowledge representations.
文摘This paper reviews a class of important models of granular computing which are induced by equivalence relations,or by general binary relations,or by neighborhood systems,and propose a class of models of granular computing which are induced by coverings of the given universe.
基金supported by grants from the National Natural Science Foundation of China(Nos.11071284 and 61075120)the Natural Science Foundation of Zhejiang Province in China(No.Y107262).
文摘In granular computing granular structures represent knowledge on universe,in this paper several important granular structures are considered.In a general granular structure the notions of interior point, accumulation point and boundary point etc are proposed,by use of these notions and referring to topological method,the lower and upper approximations of a subset of universe are defined such that they are one kind of generalization of the existing approximations based on some special granular structure.Basic properties of new rough set approximations are investigated.Furthermore,granular structures on universe are characterized by the lower and upper approximation operators.
文摘With the rapid development of the cloud computing technology, it has matured enough for a lot of individuals and organizations to move their work into the cloud. Correspondingly, a variety of cloud services are emerging. It is a key issue to assess the cloud services in order to help the cloud users select the most suitable cloud service and the cloud providers offer this service with the highest quality. The criteria parameters defining the cloud services are complex which lead to cloud service deviation. In this paper, we propose an assessment method of parameters importance in cloud services using rough set theory. The method can effectively compute the importance of cloud services parameters and sort them. On the one hand, the calculation can be used as the credible reference when users choose their appropriate cloud services. On the other hand, it can help cloud service providers to meet user requirements and enhance the user experience. The simulation results show the effectiveness of the method and its relevance in the cloud context.
文摘The two universes multi-granularity fuzzy rough set model is an effective tool for handling uncertainty problems between two domains with the help of binary fuzzy relations. This article applies the idea of neighborhood rough sets to two universes multi-granularity fuzzy rough sets, and discusses the two-universes multi-granularity neighborhood fuzzy rough set model. Firstly, the upper and lower approximation operators are defined in the two universes multi-granularity neighborhood fuzzy rough set model. Secondly, the properties of the upper and lower approximation operators are discussed. Finally, the properties of the two universes multi-granularity neighborhood fuzzy rough set model are verified through case studies.
