In view of the mid and long term runoff forecasting containing many uncertain factors,this paper constructs a uncertain reasoning model(UR)based on the cloud theory to solve the problem of uncertain reasoning.Firstly,...In view of the mid and long term runoff forecasting containing many uncertain factors,this paper constructs a uncertain reasoning model(UR)based on the cloud theory to solve the problem of uncertain reasoning.Firstly,in the proposed model,a classification method,i.e.,attribute oriented induction maximum variance(MaxVar),is used to divide the runoff series into different intervals,which are softened and described by the cloud membership with expected value(Ex),entropy(En)and hyper-entropy(He),then an uncertain reasoning rule set is constructed by means of the runoff value generalization and applied to monthly flow for uncertain prediction.Next,a new modification formula is used to calculate He in runoff forecasting,and a confident level probability prediction interval is obtained by statistical method.Finally,this paper takes the monthly flow of Manwan station in China as an example and uses UR model,LSSVM model,and ARMA model to calculate the monthly flow,respectively.The results show that the UR model has the highest prediction accuracy compared to other models,and that it not only provides random output but also supports probability interval prediction.展开更多
Uncertain Knowledge Graphs(UKGs)are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge graphs.The research on the embedding of UKG has only rec...Uncertain Knowledge Graphs(UKGs)are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge graphs.The research on the embedding of UKG has only recently begun,Uncertain Knowledge Graph Embedding(UKGE)model has a certain effect on solving this problem.However,there are still unresolved issues.On the one hand,when reasoning the confidence of unseen relation facts,the introduced probabilistic soft logic cannot be used to combine multi-path and multi-step global information,leading to information loss.On the other hand,the existing UKG embedding model can only model symmetric relation facts,but the embedding problem of asymmetric relation facts has not be addressed.To address the above issues,a Multiplex Uncertain Knowledge Graph Embedding(MUKGE)model is proposed in this paper.First,to combine multiple information and achieve more accurate results in confidence reasoning,the Uncertain ResourceRank(URR)reasoning algorithm is introduced.Second,the asymmetry in the UKG is defined.To embed asymmetric relation facts of UKG,a multi-relation embedding model is proposed.Finally,experiments are carried out on different datasets via 4 tasks to verify the effectiveness of MUKGE.The results of experiments demonstrate that MUKGE can obtain better overall performance than the baselines,and it helps advance the research on UKG embedding.展开更多
In the past, expert systems exploited mainly the EMYCIN modeland the PROSPECTOR model to deal with uncertainties. In other words, a lot ofstand-alone expert systems which use these two models are available. If we can ...In the past, expert systems exploited mainly the EMYCIN modeland the PROSPECTOR model to deal with uncertainties. In other words, a lot ofstand-alone expert systems which use these two models are available. If we can usethe Internet to couple them together, their performance will be improved throughcooperation. This is because the problem-solving ability of expert systems is greatlyimproved by the way of cooperation among different expert systems in a distributedexpert system. Cooperation between different expert systems with these two het-erogeneous uncertain reasoning models is essentially based on the transformations ofuncertainties of propositions between these two models. In this paper, we discoveredthe exactly isomorphic transformations uncertainties between uncertain reasoningmodels, as used by EMYCIN and PROSPECTOR.展开更多
基金supported by the National"Eleventh Five"Science and Technology Supporting Plan of China(Grant No.2007BAB28B01)
文摘In view of the mid and long term runoff forecasting containing many uncertain factors,this paper constructs a uncertain reasoning model(UR)based on the cloud theory to solve the problem of uncertain reasoning.Firstly,in the proposed model,a classification method,i.e.,attribute oriented induction maximum variance(MaxVar),is used to divide the runoff series into different intervals,which are softened and described by the cloud membership with expected value(Ex),entropy(En)and hyper-entropy(He),then an uncertain reasoning rule set is constructed by means of the runoff value generalization and applied to monthly flow for uncertain prediction.Next,a new modification formula is used to calculate He in runoff forecasting,and a confident level probability prediction interval is obtained by statistical method.Finally,this paper takes the monthly flow of Manwan station in China as an example and uses UR model,LSSVM model,and ARMA model to calculate the monthly flow,respectively.The results show that the UR model has the highest prediction accuracy compared to other models,and that it not only provides random output but also supports probability interval prediction.
基金the National Key Research and Development Program of China(Nos.2020YFC2003502,2021YFF0704101)the National Natural Science Foundation of China(Grant No.62276038)+1 种基金the Natural Science Foundation of Chongqing(Nos.cstc2019jcyj-cxttX0002,cstc2021ycjh-bgzxm0013)the Key Cooperation Project of Chongqing Municipal Education Commission(HZ20210-08).
文摘Uncertain Knowledge Graphs(UKGs)are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge graphs.The research on the embedding of UKG has only recently begun,Uncertain Knowledge Graph Embedding(UKGE)model has a certain effect on solving this problem.However,there are still unresolved issues.On the one hand,when reasoning the confidence of unseen relation facts,the introduced probabilistic soft logic cannot be used to combine multi-path and multi-step global information,leading to information loss.On the other hand,the existing UKG embedding model can only model symmetric relation facts,but the embedding problem of asymmetric relation facts has not be addressed.To address the above issues,a Multiplex Uncertain Knowledge Graph Embedding(MUKGE)model is proposed in this paper.First,to combine multiple information and achieve more accurate results in confidence reasoning,the Uncertain ResourceRank(URR)reasoning algorithm is introduced.Second,the asymmetry in the UKG is defined.To embed asymmetric relation facts of UKG,a multi-relation embedding model is proposed.Finally,experiments are carried out on different datasets via 4 tasks to verify the effectiveness of MUKGE.The results of experiments demonstrate that MUKGE can obtain better overall performance than the baselines,and it helps advance the research on UKG embedding.
文摘In the past, expert systems exploited mainly the EMYCIN modeland the PROSPECTOR model to deal with uncertainties. In other words, a lot ofstand-alone expert systems which use these two models are available. If we can usethe Internet to couple them together, their performance will be improved throughcooperation. This is because the problem-solving ability of expert systems is greatlyimproved by the way of cooperation among different expert systems in a distributedexpert system. Cooperation between different expert systems with these two het-erogeneous uncertain reasoning models is essentially based on the transformations ofuncertainties of propositions between these two models. In this paper, we discoveredthe exactly isomorphic transformations uncertainties between uncertain reasoningmodels, as used by EMYCIN and PROSPECTOR.
