A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that dece...A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that decentralized BP neural networks are used to adaptively learn the uncertainty bounds of interconnected subsystems in the Lyapunov sense, and the outputs of the decentralized BP neural networks are then used as the parameters of the sliding mode controller to compensate for the effects of subsystems uncertainties. Using this scheme, not only strong robustness with respect to uncertainty dynamics and nonlinearities can be obtained, but also the output tracking error between the actual output of each subsystem and the corresponding desired reference output can asymptotically converge to zero. A simulation example is presented to support the validity of the proposed BP neural-networks-based sliding mode controller.展开更多
This paper investigated the resistance performance of a submersible surface ship(SSS)in different working cases and scales to analyze the hydrodynamic performance characteristics of an SSS at different speeds and divi...This paper investigated the resistance performance of a submersible surface ship(SSS)in different working cases and scales to analyze the hydrodynamic performance characteristics of an SSS at different speeds and diving depths for engineering applications.First,a hydrostatic resistance performance test of the SSS was carried out in a towing tank.Second,the scale effect of the hydrodynamic pressure coefficient and wave-making resistance was analyzed.The differences between the three-dimensional real-scale ship resistance prediction and numerical methods were explained.Finally,the advantages of genetic algorithm(GA)and neural network were combined to predict the resistance of SSS.Back propagation neural network(BPNN)and GA-BPNN were utilized to predict the SSS resistance.We also studied neural network parameter optimization,including connection weights and thresholds,using K-fold cross-validation.The results showed that when a SSS sails at low and medium speeds,the influence of various underwater cases on resistance is not obvious,while at high speeds,the resistance of water surface cases increases sharply with an increase in speed.After improving the weights and thresholds through K-fold cross-validation and GA,the prediction results of BPNN have high consistency with the actual values.The research results can provide a theoretical reference for the optimal design of the resistance of SSS in practical applications.展开更多
Objective:To analyze the factors related to vessel vasovagal reaction(VVR)in apheresis donors,establish a mathematical model for predicting the correlation factors and occurrence risk,and use the prediction model to i...Objective:To analyze the factors related to vessel vasovagal reaction(VVR)in apheresis donors,establish a mathematical model for predicting the correlation factors and occurrence risk,and use the prediction model to intervene in high-risk VVR blood donors,improve the blood donation experience,and retain blood donors.Methods:A total of 316 blood donors from the Xi'an Central Blood Bank from June to September 2022 were selected to statistically analyze VVR-related factors.A BP neural network prediction model is established with relevant factors as input and DRVR risk as output.Results:First-time blood donors had a high risk of VVR,female risk was high,and sex difference was significant(P value<0.05).The blood pressure before donation and intergroup differences were also significant(P value<0.05).After training,the established BP neural network model has a minimum RMS error of o.116,a correlation coefficient R=0.75,and a test model accuracy of 66.7%.Conclusion:First-time blood donors,women,and relatively low blood pressure are all high-risk groups for VVR.The BP neural network prediction model established in this paper has certain prediction accuracy and can be used as a means to evaluate the risk degree of clinical blood donors.展开更多
The distribution characteristics of rare earth elements （REE） in bottomsediments are influenced by many factors. Hence, conducting a quantitative analysis isdifficult. A qualitative analysis of the relationships bet...The distribution characteristics of rare earth elements （REE） in bottomsediments are influenced by many factors. Hence, conducting a quantitative analysis isdifficult. A qualitative analysis of the relationships between ΣREE content andprovenance, hydrodynamics, grain size and mineral distribution in the Beibu Gulf showsthat terrestrial rocks control the ΣREE composition. Both weaker hydrodynamics andfiner grain size lead to a higher ΣREE content. Relative curves revealing therelationships between individual impact factors and ΣREE content were obtained fromthe combination of qualitative and quantitative analyses of the BP neural network,which trained the position of samples, gravel content, sand content, silt content, claycontent and clay mineral content. The results are consistent with those of thequantitative analysis. The self-learning algorithm is automatically determined andcalculated quantitatively. The impact of each factor on REEs and how each factorcontrols the ΣREE distribution is identified. Thus, environmental changes and thegeological evolution of the region can be inferred based on curve variation and the geological evolution of the region can be inferred based on curve variation and theactual situation. This method also provides useful theoretical guidance for the analysisof REE enrichment and dispersion.展开更多
基金The National Natural Science Foundations of China(50505029)
文摘A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that decentralized BP neural networks are used to adaptively learn the uncertainty bounds of interconnected subsystems in the Lyapunov sense, and the outputs of the decentralized BP neural networks are then used as the parameters of the sliding mode controller to compensate for the effects of subsystems uncertainties. Using this scheme, not only strong robustness with respect to uncertainty dynamics and nonlinearities can be obtained, but also the output tracking error between the actual output of each subsystem and the corresponding desired reference output can asymptotically converge to zero. A simulation example is presented to support the validity of the proposed BP neural-networks-based sliding mode controller.
文摘This paper investigated the resistance performance of a submersible surface ship(SSS)in different working cases and scales to analyze the hydrodynamic performance characteristics of an SSS at different speeds and diving depths for engineering applications.First,a hydrostatic resistance performance test of the SSS was carried out in a towing tank.Second,the scale effect of the hydrodynamic pressure coefficient and wave-making resistance was analyzed.The differences between the three-dimensional real-scale ship resistance prediction and numerical methods were explained.Finally,the advantages of genetic algorithm(GA)and neural network were combined to predict the resistance of SSS.Back propagation neural network(BPNN)and GA-BPNN were utilized to predict the SSS resistance.We also studied neural network parameter optimization,including connection weights and thresholds,using K-fold cross-validation.The results showed that when a SSS sails at low and medium speeds,the influence of various underwater cases on resistance is not obvious,while at high speeds,the resistance of water surface cases increases sharply with an increase in speed.After improving the weights and thresholds through K-fold cross-validation and GA,the prediction results of BPNN have high consistency with the actual values.The research results can provide a theoretical reference for the optimal design of the resistance of SSS in practical applications.
基金Xi'an Municipal Bureau of Science and Technology,Science and Technology Program,Medical Research Project。
文摘Objective:To analyze the factors related to vessel vasovagal reaction(VVR)in apheresis donors,establish a mathematical model for predicting the correlation factors and occurrence risk,and use the prediction model to intervene in high-risk VVR blood donors,improve the blood donation experience,and retain blood donors.Methods:A total of 316 blood donors from the Xi'an Central Blood Bank from June to September 2022 were selected to statistically analyze VVR-related factors.A BP neural network prediction model is established with relevant factors as input and DRVR risk as output.Results:First-time blood donors had a high risk of VVR,female risk was high,and sex difference was significant(P value<0.05).The blood pressure before donation and intergroup differences were also significant(P value<0.05).After training,the established BP neural network model has a minimum RMS error of o.116,a correlation coefficient R=0.75,and a test model accuracy of 66.7%.Conclusion:First-time blood donors,women,and relatively low blood pressure are all high-risk groups for VVR.The BP neural network prediction model established in this paper has certain prediction accuracy and can be used as a means to evaluate the risk degree of clinical blood donors.
文摘The distribution characteristics of rare earth elements （REE） in bottomsediments are influenced by many factors. Hence, conducting a quantitative analysis isdifficult. A qualitative analysis of the relationships between ΣREE content andprovenance, hydrodynamics, grain size and mineral distribution in the Beibu Gulf showsthat terrestrial rocks control the ΣREE composition. Both weaker hydrodynamics andfiner grain size lead to a higher ΣREE content. Relative curves revealing therelationships between individual impact factors and ΣREE content were obtained fromthe combination of qualitative and quantitative analyses of the BP neural network,which trained the position of samples, gravel content, sand content, silt content, claycontent and clay mineral content. The results are consistent with those of thequantitative analysis. The self-learning algorithm is automatically determined andcalculated quantitatively. The impact of each factor on REEs and how each factorcontrols the ΣREE distribution is identified. Thus, environmental changes and thegeological evolution of the region can be inferred based on curve variation and the geological evolution of the region can be inferred based on curve variation and theactual situation. This method also provides useful theoretical guidance for the analysisof REE enrichment and dispersion.
