By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Com...By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid.展开更多
In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural...In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural network proportion-integration-differentiation （PID） control parameters on-line adjustment is utilized to improve system accuracy, celerity and stability. Simulation results indicate that with the control system proposed in this paper, the system deviation is reduced, therefore accuracy is improved; response speed for step signal and sinusoidal signal gets faster, thus acceleration is rapidly improved; and the system can be restored to the control value in case of interfering, so stability is improved.展开更多
Adaptive flight control technology, feedback linearization, model inversion theory are reviewed and the error dynamic characteristics are analyzed, and an adaptive on-line neural network attitude control system is pre...Adaptive flight control technology, feedback linearization, model inversion theory are reviewed and the error dynamic characteristics are analyzed, and an adaptive on-line neural network attitude control system is presented. The model inversion is under the hover condition. And the adaptive control law based on the neural network is designed to guarantee the boundedness of tracking error and control signals. Simulation results demonstrate that the nonlinear neural network augmented model inversion can self-adapt to the uncertainty and modeling errors of unmanned helicopters. Results are compared while the parameters of PD controller and robustness items are changed.展开更多
This paper presents a new type of cellular automa ta (CA) model for the simulation of alternative land development using neural netw orks for urban planning. CA models can be regarded as a planning tool because th ey ...This paper presents a new type of cellular automa ta (CA) model for the simulation of alternative land development using neural netw orks for urban planning. CA models can be regarded as a planning tool because th ey can generate alternative urban growth. Alternative development patterns can b e formed by using different sets of parameter values in CA simulation. A critica l issue is how to define parameter values for realistic and idealized simulation . This paper demonstrates that neural networks can simplify CA models but genera te more plausible results. The simulation is based on a simple three-layer netw ork with an output neuron to generate conversion probability. No transition rule s are required for the simulation. Parameter values are automatically obtained f rom the training of network by using satellite remote sensing data. Original tra ining data can be assessed and modified according to planning objectives. Altern ative urban patterns can be easily formulated by using the modified training dat a sets rather than changing the model.展开更多
Prediction and sensitivity models,to elucidate the response of phytoplankton biomass to environmental factors in Quanzhou Bay,Fujian,China,were developed using a back propagation(BP) network.The environmental indicato...Prediction and sensitivity models,to elucidate the response of phytoplankton biomass to environmental factors in Quanzhou Bay,Fujian,China,were developed using a back propagation(BP) network.The environmental indicators of coastal phytoplankton biomass were determined and monitoring data for the bay from 2008 was used to train,test and build a three-layer BP artificial neural network with multi-input and single-output.Ten water quality parameters were used to forecast phytoplankton biomass(measured as chlorophyll-a concentration).Correlation coefficient between biomass values predicted by the model and those observed was 0.964,whilst the average relative error of the network was-3.46% and average absolute error was 10.53%.The model thus has high level of accuracy and is suitable for analysis of the influence of aquatic environmental factors on phytoplankton biomass.A global sensitivity analysis was performed to determine the influence of different environmental indicators on phytoplankton biomass.Indicators were classified according to the sensitivity of response and its risk degree.The results indicate that the parameters most relevant to phytoplankton biomass are estuary-related and include pH,sea surface temperature,sea surface salinity,chemical oxygen demand and ammonium.展开更多
A fully coupled 6-degree-of-freedom nonlinear dynamic model is presented to analyze the dynamic response of a semi-submersible platform which is equipped with the dynamic positioning(DP) system. In the control force d...A fully coupled 6-degree-of-freedom nonlinear dynamic model is presented to analyze the dynamic response of a semi-submersible platform which is equipped with the dynamic positioning(DP) system. In the control force design, a dynamic model of reference linear drift frequency in the horizontal plane is introduced. The dynamic surface control(DSC) is used to design a control strategy for the DP. Compared with the traditional back-stepping methods, the dynamic surface control combined with radial basis function(RBF) neural networks(NNs) can avoid differentiating intermediate variables repeatedly in every design step due to the introduction of a first order filter. Low frequency motions obtained from total motions by a low pass filter are chosen to be the inputs for the RBF NNs which are used to approximate the low frequency wave force. Considering the propellers' wear and tear, the effect of filtering frequencies for the control force is discussed. Based on power consumptions and positioning requirements, the NN centers are determined. Moreover, the RBF NNs used to approximate the total wave force are built to monitor the disturbances. With the DP assistance, the results of fully coupled dynamic response simulations are given to illustrate the effectiveness of the proposed control strategy.展开更多
Digital valve control servo system is studied in this paper. In order to solve the system problems of poor control precision and slow response time,a CMAC-PID( cerebellar model articulation controller-PID) compound co...Digital valve control servo system is studied in this paper. In order to solve the system problems of poor control precision and slow response time,a CMAC-PID( cerebellar model articulation controller-PID) compound control method is proposed. This compound controller consists of two components: one is a traditional PID for the feedback control to guarantee stability of the system; the other is the CMAC control algorithm to form a feed-forward control for achieving high control precision and short response time of the controlled plant. Then the CMAC-PID compound control method is used in the digital valve control servo system to improve its control performance. Through simulation and experiment,the proposed CMAC-PID compound control method is superior to the traditional PID control for enhancing stability and robustness,and thus this compound control can be used as a new control strategy for the digital valve control servo system.展开更多
文摘By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid.
文摘In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural network proportion-integration-differentiation （PID） control parameters on-line adjustment is utilized to improve system accuracy, celerity and stability. Simulation results indicate that with the control system proposed in this paper, the system deviation is reduced, therefore accuracy is improved; response speed for step signal and sinusoidal signal gets faster, thus acceleration is rapidly improved; and the system can be restored to the control value in case of interfering, so stability is improved.
文摘Adaptive flight control technology, feedback linearization, model inversion theory are reviewed and the error dynamic characteristics are analyzed, and an adaptive on-line neural network attitude control system is presented. The model inversion is under the hover condition. And the adaptive control law based on the neural network is designed to guarantee the boundedness of tracking error and control signals. Simulation results demonstrate that the nonlinear neural network augmented model inversion can self-adapt to the uncertainty and modeling errors of unmanned helicopters. Results are compared while the parameters of PD controller and robustness items are changed.
文摘This paper presents a new type of cellular automa ta (CA) model for the simulation of alternative land development using neural netw orks for urban planning. CA models can be regarded as a planning tool because th ey can generate alternative urban growth. Alternative development patterns can b e formed by using different sets of parameter values in CA simulation. A critica l issue is how to define parameter values for realistic and idealized simulation . This paper demonstrates that neural networks can simplify CA models but genera te more plausible results. The simulation is based on a simple three-layer netw ork with an output neuron to generate conversion probability. No transition rule s are required for the simulation. Parameter values are automatically obtained f rom the training of network by using satellite remote sensing data. Original tra ining data can be assessed and modified according to planning objectives. Altern ative urban patterns can be easily formulated by using the modified training dat a sets rather than changing the model.
基金Supported by the Ocean Public Welfare Scientific Research Project,State Oceanic Administration of China(No.200705029)the National Special Fund for Basic Science and Technology of China(No.2012FY112500)the National Non-profit Institute Basic Research Fund(No.FIO2011T06)
文摘Prediction and sensitivity models,to elucidate the response of phytoplankton biomass to environmental factors in Quanzhou Bay,Fujian,China,were developed using a back propagation(BP) network.The environmental indicators of coastal phytoplankton biomass were determined and monitoring data for the bay from 2008 was used to train,test and build a three-layer BP artificial neural network with multi-input and single-output.Ten water quality parameters were used to forecast phytoplankton biomass(measured as chlorophyll-a concentration).Correlation coefficient between biomass values predicted by the model and those observed was 0.964,whilst the average relative error of the network was-3.46% and average absolute error was 10.53%.The model thus has high level of accuracy and is suitable for analysis of the influence of aquatic environmental factors on phytoplankton biomass.A global sensitivity analysis was performed to determine the influence of different environmental indicators on phytoplankton biomass.Indicators were classified according to the sensitivity of response and its risk degree.The results indicate that the parameters most relevant to phytoplankton biomass are estuary-related and include pH,sea surface temperature,sea surface salinity,chemical oxygen demand and ammonium.
基金funded by the National Basic Research Program of China (Grant Nos. 2011CB013702 and 2011CB013703)the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant No. 50921001)
文摘A fully coupled 6-degree-of-freedom nonlinear dynamic model is presented to analyze the dynamic response of a semi-submersible platform which is equipped with the dynamic positioning(DP) system. In the control force design, a dynamic model of reference linear drift frequency in the horizontal plane is introduced. The dynamic surface control(DSC) is used to design a control strategy for the DP. Compared with the traditional back-stepping methods, the dynamic surface control combined with radial basis function(RBF) neural networks(NNs) can avoid differentiating intermediate variables repeatedly in every design step due to the introduction of a first order filter. Low frequency motions obtained from total motions by a low pass filter are chosen to be the inputs for the RBF NNs which are used to approximate the low frequency wave force. Considering the propellers' wear and tear, the effect of filtering frequencies for the control force is discussed. Based on power consumptions and positioning requirements, the NN centers are determined. Moreover, the RBF NNs used to approximate the total wave force are built to monitor the disturbances. With the DP assistance, the results of fully coupled dynamic response simulations are given to illustrate the effectiveness of the proposed control strategy.
基金Supported by the National Natural Science Foundation of China(No.51505412)the Independent Study Program for Young Teachers in Yanshan University(No.14LGB004)
文摘Digital valve control servo system is studied in this paper. In order to solve the system problems of poor control precision and slow response time,a CMAC-PID( cerebellar model articulation controller-PID) compound control method is proposed. This compound controller consists of two components: one is a traditional PID for the feedback control to guarantee stability of the system; the other is the CMAC control algorithm to form a feed-forward control for achieving high control precision and short response time of the controlled plant. Then the CMAC-PID compound control method is used in the digital valve control servo system to improve its control performance. Through simulation and experiment,the proposed CMAC-PID compound control method is superior to the traditional PID control for enhancing stability and robustness,and thus this compound control can be used as a new control strategy for the digital valve control servo system.
