Although evidence theory has been widely used in information fusion due to its effectiveness of uncertainty reasoning, the classical DS evidence theory involves counter-intuitive behaviors when high conflict informati...Although evidence theory has been widely used in information fusion due to its effectiveness of uncertainty reasoning, the classical DS evidence theory involves counter-intuitive behaviors when high conflict information exists. Many modification methods have been developed which can be classified into the following two kinds of ideas, either modifying the combination rules or modifying the evidence sources. In order to make the modification more reasonable and more effective, this paper gives a thorough analysis of some typical existing modification methods firstly, and then extracts the intrinsic feature of the evidence sources by using evidence distance theory. Based on the extracted features, two modified plans of evidence theory according to the corresponding modification ideas have been proposed. The results of numerical examples prove the good performance of the plans when combining evidence sources with high conflict information.展开更多
This paper is a research on the characteristics of power big data. According to the characteristics of ＂large volume＂, ＂species diversity＂, ＂sparse value density＂, ＂fast speed＂ of the power big data, a predict...This paper is a research on the characteristics of power big data. According to the characteristics of ＂large volume＂, ＂species diversity＂, ＂sparse value density＂, ＂fast speed＂ of the power big data, a prediction model of multi-source information fusion for large data is established, the fusion prediction of various parameters of the same object is realized. A combined algorithm of Map Reduce and neural network is used in this paper. Using clustering and nonlinear mapping ability of neural network, it can effectively solve the problem of nonlinear objective function approximation, and neural network is applied to the prediction of fusion. In this paper, neural network model using multi layer feed forward network--BP neural network. Simultaneously, to achieve large-scale data sets in parallel computing, the parallelism and real-time property of the algorithm should be considered, further combined with Reduce Map model, to realize the parallel processing of the algorithm, making it more suitable for the study of the fusion of large data. And finally, through simulation, it verifies the feasibility of the proposed model and algorithm.展开更多
To solve low precision and poor stability of the extended Kalman filter （EKF） in the vehicle integrated positioning system owing to acceleration, deceleration and turning （hereinafter referred to as maneuvering） ,...To solve low precision and poor stability of the extended Kalman filter （EKF） in the vehicle integrated positioning system owing to acceleration, deceleration and turning （hereinafter referred to as maneuvering） , the paper presents an adaptive filter algorithm that combines interacting multiple model （IMM） and non linear Kalman filter. The algorithm describes the motion mode of vehicle by using three state spacemode]s. At first, the parallel filter of each model is realized by using multiple nonlinear filters. Then the weight integration of filtering result is carried out by using the model matching likelihood function so as to get the system positioning information. The method has advantages of nonlinear system filter and overcomes disadvantages of single model of filtering algorithm that has poor effects on positioning the maneuvering target. At last, the paper uses IMM and EKF methods to simulate the global positioning system （OPS）/inertial navigation system （INS）/dead reckoning （DR） integrated positioning system, respectively. The results indicate that the IMM algorithm is obviously superior to EKF filter used in the integrated positioning system at present. Moreover, it can greatly enhance the stability and positioning precision of integrated positioning system.展开更多
文摘Although evidence theory has been widely used in information fusion due to its effectiveness of uncertainty reasoning, the classical DS evidence theory involves counter-intuitive behaviors when high conflict information exists. Many modification methods have been developed which can be classified into the following two kinds of ideas, either modifying the combination rules or modifying the evidence sources. In order to make the modification more reasonable and more effective, this paper gives a thorough analysis of some typical existing modification methods firstly, and then extracts the intrinsic feature of the evidence sources by using evidence distance theory. Based on the extracted features, two modified plans of evidence theory according to the corresponding modification ideas have been proposed. The results of numerical examples prove the good performance of the plans when combining evidence sources with high conflict information.
文摘This paper is a research on the characteristics of power big data. According to the characteristics of ＂large volume＂, ＂species diversity＂, ＂sparse value density＂, ＂fast speed＂ of the power big data, a prediction model of multi-source information fusion for large data is established, the fusion prediction of various parameters of the same object is realized. A combined algorithm of Map Reduce and neural network is used in this paper. Using clustering and nonlinear mapping ability of neural network, it can effectively solve the problem of nonlinear objective function approximation, and neural network is applied to the prediction of fusion. In this paper, neural network model using multi layer feed forward network--BP neural network. Simultaneously, to achieve large-scale data sets in parallel computing, the parallelism and real-time property of the algorithm should be considered, further combined with Reduce Map model, to realize the parallel processing of the algorithm, making it more suitable for the study of the fusion of large data. And finally, through simulation, it verifies the feasibility of the proposed model and algorithm.
基金National Natural Science Foundation of China(No.61663020)Project of Education Department of Gansu Province(No.2016B-036)
文摘To solve low precision and poor stability of the extended Kalman filter （EKF） in the vehicle integrated positioning system owing to acceleration, deceleration and turning （hereinafter referred to as maneuvering） , the paper presents an adaptive filter algorithm that combines interacting multiple model （IMM） and non linear Kalman filter. The algorithm describes the motion mode of vehicle by using three state spacemode]s. At first, the parallel filter of each model is realized by using multiple nonlinear filters. Then the weight integration of filtering result is carried out by using the model matching likelihood function so as to get the system positioning information. The method has advantages of nonlinear system filter and overcomes disadvantages of single model of filtering algorithm that has poor effects on positioning the maneuvering target. At last, the paper uses IMM and EKF methods to simulate the global positioning system （OPS）/inertial navigation system （INS）/dead reckoning （DR） integrated positioning system, respectively. The results indicate that the IMM algorithm is obviously superior to EKF filter used in the integrated positioning system at present. Moreover, it can greatly enhance the stability and positioning precision of integrated positioning system.
