In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself （including fuzziness, randomness, incompleteness as well as ...In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself （including fuzziness, randomness, incompleteness as well as roughness, etc）. Hence it requires investigating the problem of uncertain information fusion. Robust learning algorithm which adapts to complex environment and the fuzzy inference algorithm which disposes fuzzy information are explored to solve the problem. Based on the fusion technology of neural networks and fuzzy inference algorithm, a multi-sensor uncertain information fusion system is modeled. Also RANFIS learning algorithm and fusing weight synthesized inference algorithm are developed from the ANFIS algorithm according to the concept of robust neural networks. This fusion system mainly consists of RANFIS confidence estimator, fusing weight synthesized inference knowledge base and weighted fusion section. The simulation result demonstrates that the proposed fusion model and algorithm have the capability of uncertain information fusion, thus is obviously advantageous compared with the conventional Kalman weighted fusion algorithm.展开更多
Remanufacturing,as one of the optimal disposals of end-of-life products,can bring tremendous economic and ecological benefits.Remanufacturing process planning is facing an immense challenge due to uncertainties and fu...Remanufacturing,as one of the optimal disposals of end-of-life products,can bring tremendous economic and ecological benefits.Remanufacturing process planning is facing an immense challenge due to uncertainties and fuzziness of recoverable products in damage conditions and remanufacturing quality requirements.Although researchers have studied the influence of uncertainties on remanufacturing process planning,very few of them comprehensively studied the interactions among damage conditions and quality requirements that involve uncertain,fuzzy information.Hence,this challenge in the context of uncertain,fuzzy information is undertaken in this paper,and a method for remanufacturing process planning is presented to maximize remanufacturing efficiency and minimize cost.In particular,the characteristics of uncertainties and fuzziness involved in the remanufacturing processes are explicitly analyzed.An optimization model is then developed to minimize remanufacturing time and cost.The solution is provided through an improved Takagi-Sugeno fuzzy neural network(T-S FNN)method.The effectiveness of the proposed approach is exemplified and elucidated by a case study.Results show that the training speed and accuracy of the improved T-S FNN method are 23.5%and 82.5%higher on average than those of the original method,respectively.展开更多
According to the soundness and completeness of information in databases, the expressive form and the semantics of incomplete information are discussed in this paper. On the basis of the discussion, the current studies...According to the soundness and completeness of information in databases, the expressive form and the semantics of incomplete information are discussed in this paper. On the basis of the discussion, the current studies on incomplete data in relational databases are reviewed. In order to represent stochastic uncertainty in most general sense in the real world, probabilistic data are introduced into relational databases. An extended relational data model is presented to express and manipulate probabilistic data and the operations in relational algebra based on the extended model are defined in this paper.展开更多
基金This project was supported by the National Natural Science Foundation of China (60572038)
文摘In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself （including fuzziness, randomness, incompleteness as well as roughness, etc）. Hence it requires investigating the problem of uncertain information fusion. Robust learning algorithm which adapts to complex environment and the fuzzy inference algorithm which disposes fuzzy information are explored to solve the problem. Based on the fusion technology of neural networks and fuzzy inference algorithm, a multi-sensor uncertain information fusion system is modeled. Also RANFIS learning algorithm and fusing weight synthesized inference algorithm are developed from the ANFIS algorithm according to the concept of robust neural networks. This fusion system mainly consists of RANFIS confidence estimator, fusing weight synthesized inference knowledge base and weighted fusion section. The simulation result demonstrates that the proposed fusion model and algorithm have the capability of uncertain information fusion, thus is obviously advantageous compared with the conventional Kalman weighted fusion algorithm.
基金This work was supported in part by the National Natural Science Foundation of China(Grant No.51975075)the National Major Scientific and Technological Special Project,China(Grant No.2019ZX04005-001)the Chongqing Technology Innovation and Application Program,China(Grant No.cstc2020jscx-msxmX0221).
文摘Remanufacturing,as one of the optimal disposals of end-of-life products,can bring tremendous economic and ecological benefits.Remanufacturing process planning is facing an immense challenge due to uncertainties and fuzziness of recoverable products in damage conditions and remanufacturing quality requirements.Although researchers have studied the influence of uncertainties on remanufacturing process planning,very few of them comprehensively studied the interactions among damage conditions and quality requirements that involve uncertain,fuzzy information.Hence,this challenge in the context of uncertain,fuzzy information is undertaken in this paper,and a method for remanufacturing process planning is presented to maximize remanufacturing efficiency and minimize cost.In particular,the characteristics of uncertainties and fuzziness involved in the remanufacturing processes are explicitly analyzed.An optimization model is then developed to minimize remanufacturing time and cost.The solution is provided through an improved Takagi-Sugeno fuzzy neural network(T-S FNN)method.The effectiveness of the proposed approach is exemplified and elucidated by a case study.Results show that the training speed and accuracy of the improved T-S FNN method are 23.5%and 82.5%higher on average than those of the original method,respectively.
文摘According to the soundness and completeness of information in databases, the expressive form and the semantics of incomplete information are discussed in this paper. On the basis of the discussion, the current studies on incomplete data in relational databases are reviewed. In order to represent stochastic uncertainty in most general sense in the real world, probabilistic data are introduced into relational databases. An extended relational data model is presented to express and manipulate probabilistic data and the operations in relational algebra based on the extended model are defined in this paper.
