Young’s modulus(YM)of intact rock is an important parameter in the assessment of engineering behaviours of rock masses,and it cannot always be obtained in an economical and practical manner in laboratory experiments....Young’s modulus(YM)of intact rock is an important parameter in the assessment of engineering behaviours of rock masses,and it cannot always be obtained in an economical and practical manner in laboratory experiments.The main purpose of this study is to examine the capability of the minimax probability machine regression(MPMR),relevance vector machine(RVM),and generalised regression neural network(GRNN)models for the prediction of YM.The other aim is to determine the usefulness of a new index,the n-durability index(ndrb),which is based on porosity and the slake durability index.According to the regression analysis performed in this study,the n-durability index as an explanatory parameter performs better than the P-wave velocity(Vp),porosity,and slake durability index in the models,considering the results herein as well as the existing literature.According to regression error characteristic curves,Taylor diagrams,and performance indices,the best prediction model is MPMR,while the worst is the GRNN model.Although GRNN is the worst of the soft computing models,its performance is slightly better than that of the multiple linear regression(MLR)model.According to the results of the study,the MPMR and RVM models with ndrb and Vp are successful tools that can predict the YM of igneous rock materials to different degrees.展开更多
Minimax probability machine regression （MPMR） was proposed for chaotic load time series global prediction. In MPMR, regression function maximizes the minimum probability that future predication will be within an ε ...Minimax probability machine regression （MPMR） was proposed for chaotic load time series global prediction. In MPMR, regression function maximizes the minimum probability that future predication will be within an ε to the true regression function. After exploring the principle of MPMR, and verifying the chaotic property of the load series from a certain power system, one-day-ahead predictions for 24 time points next day wcre done with MPMR. Thc results demonstrate that MPMP has satisfactory prediction efficiency. Kernel function shape parameter and regression tube value may influence the MPMR-based system performance. In the experiments, cross validation was used to choose the two parameters.展开更多
This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression(MPMR),Particle Swarm Optimization based Artificial Neural Network(ANN-PSO)and Particle Swarm...This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression(MPMR),Particle Swarm Optimization based Artificial Neural Network(ANN-PSO)and Particle Swarm Optimization based Adaptive Network Fuzzy Inference System(ANFIS-PSO)to study the shallow foundation reliability based on settlement criteria.Soil is a heterogeneous medium and the involvement of its attributes for geotechnical behaviour in soil-foundation system makes the prediction of settlement of shallow a complex engineering problem.This study explores the feasibility of soft computing techniques against the deterministic approach.The settlement of shallow foundation depends on the parametersγ(unit weight),e0(void ratio)and CC(compression index).These soil parameters are taken as input variables while the settlement of shallow foundation as output.To assess the performance of models,different performance indices i.e.RMSE,VAF,R^2,Bias Factor,MAPE,LMI,U(95),RSR,NS,RPD,etc.were used.From the analysis of results,it was found that MPMR model outperformed PSO-ANFIS and PSO-ANN.Therefore,MPMR can be used as a reliable soft computing technique for non-linear problems for settlement of shallow foundations on soils.展开更多
Long-term prediction of chaotic time series is very difficult,for the Chaos restricts predictability.in this paper a new method is studied to model and predict chaotic time series based on minimax probability machine ...Long-term prediction of chaotic time series is very difficult,for the Chaos restricts predictability.in this paper a new method is studied to model and predict chaotic time series based on minimax probability machine regression （MPMR）. Since the positive global Lyapunov exponents lead the errors to increase exponentially in modelling the chaotic time series, a weighted term is introduced to compensate a cost function. Using mean square error （MSE） and absolute error （AE） as a criterion, simulation results show that the proposed method is more effective and accurate for multistep prediction. It can identify the system characteristics quite well and provide a new way to make long-term predictions of the chaotic time series.展开更多
文摘Young’s modulus(YM)of intact rock is an important parameter in the assessment of engineering behaviours of rock masses,and it cannot always be obtained in an economical and practical manner in laboratory experiments.The main purpose of this study is to examine the capability of the minimax probability machine regression(MPMR),relevance vector machine(RVM),and generalised regression neural network(GRNN)models for the prediction of YM.The other aim is to determine the usefulness of a new index,the n-durability index(ndrb),which is based on porosity and the slake durability index.According to the regression analysis performed in this study,the n-durability index as an explanatory parameter performs better than the P-wave velocity(Vp),porosity,and slake durability index in the models,considering the results herein as well as the existing literature.According to regression error characteristic curves,Taylor diagrams,and performance indices,the best prediction model is MPMR,while the worst is the GRNN model.Although GRNN is the worst of the soft computing models,its performance is slightly better than that of the multiple linear regression(MLR)model.According to the results of the study,the MPMR and RVM models with ndrb and Vp are successful tools that can predict the YM of igneous rock materials to different degrees.
基金The research was supported by the Science & Research Foundation of East China Jiaotong University (No.23)
文摘Minimax probability machine regression （MPMR） was proposed for chaotic load time series global prediction. In MPMR, regression function maximizes the minimum probability that future predication will be within an ε to the true regression function. After exploring the principle of MPMR, and verifying the chaotic property of the load series from a certain power system, one-day-ahead predictions for 24 time points next day wcre done with MPMR. Thc results demonstrate that MPMP has satisfactory prediction efficiency. Kernel function shape parameter and regression tube value may influence the MPMR-based system performance. In the experiments, cross validation was used to choose the two parameters.
基金financially supported by High-end Foreign Expert program(G20190022002)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJZDK201900102)Chongqing Construction Science and Technology Plan Project(2019-0045),that are gratefully acknowledged。
文摘This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression(MPMR),Particle Swarm Optimization based Artificial Neural Network(ANN-PSO)and Particle Swarm Optimization based Adaptive Network Fuzzy Inference System(ANFIS-PSO)to study the shallow foundation reliability based on settlement criteria.Soil is a heterogeneous medium and the involvement of its attributes for geotechnical behaviour in soil-foundation system makes the prediction of settlement of shallow a complex engineering problem.This study explores the feasibility of soft computing techniques against the deterministic approach.The settlement of shallow foundation depends on the parametersγ(unit weight),e0(void ratio)and CC(compression index).These soil parameters are taken as input variables while the settlement of shallow foundation as output.To assess the performance of models,different performance indices i.e.RMSE,VAF,R^2,Bias Factor,MAPE,LMI,U(95),RSR,NS,RPD,etc.were used.From the analysis of results,it was found that MPMR model outperformed PSO-ANFIS and PSO-ANN.Therefore,MPMR can be used as a reliable soft computing technique for non-linear problems for settlement of shallow foundations on soils.
基金Project supported by the National Natural Science Foundation of China （Grant No 60602034） and the Natural Science Foundation of Jiangxi Province, China （Grant No 0611031）.
文摘Long-term prediction of chaotic time series is very difficult,for the Chaos restricts predictability.in this paper a new method is studied to model and predict chaotic time series based on minimax probability machine regression （MPMR）. Since the positive global Lyapunov exponents lead the errors to increase exponentially in modelling the chaotic time series, a weighted term is introduced to compensate a cost function. Using mean square error （MSE） and absolute error （AE） as a criterion, simulation results show that the proposed method is more effective and accurate for multistep prediction. It can identify the system characteristics quite well and provide a new way to make long-term predictions of the chaotic time series.
