Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a dis...Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm(DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly,the information processing strength(IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm(DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIASORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.展开更多
For a single machine infinite power system with thyristor controlled series compensation(TCSC) device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we prese...For a single machine infinite power system with thyristor controlled series compensation(TCSC) device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network(RBFNN). The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error,which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function,which can guarantee the uniform ultimate boundedness(UUB) of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.展开更多
Flight risk prediction is significant in improving the flight crew's situational awareness because it allows them to adopt appropriate operation strategies to prevent risk expansion caused by abnormal conditions,e...Flight risk prediction is significant in improving the flight crew's situational awareness because it allows them to adopt appropriate operation strategies to prevent risk expansion caused by abnormal conditions,especially aircraft icing conditions.The flight risk space representing the nonlinear mapping relations between risk degree and the three-dimensional commanded vector(commanded airspeed,commanded bank angle,and commanded vertical velocity)is developed to provide the crew with practical risk information.However,the construction of flight risk space by means of computational flight dynamics suffers from certain defects,including slow computing speed.Accordingly,an intelligent approach for flight risk prediction is proposed to address these defects based on neural networks.Radial Basis Function Neural Network(RBFNN)is optimized using Adaptive Particle Swarm Optimization(APSO).To optimize both the parameters and the structure of APSO-RBFNN,a fitness function containing the training accuracy and network structure size is proposed.Extensive experimental results demonstrate that the flight risk predicted by APSO-RBFNN is very close to that obtained via computational flight dynamics.The average error(RMSE)is less than 10^(-1).The approach achieves a speedup close to 1000x compared with computational flight dynamics.In addition,some flight upset and recovery cases are presented to illustrate the efficiency of the intelligent approach for flight risk prediction.展开更多
A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious ta...A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data.Thus,in this paper,a Radial Basis Function Neural Network(RBFNN)trained using Manta Ray Foraging Optimization algorithm(MRFO)is proposed.RBFNN is a three-layer network comprising of input,output,and hidden layers that can process large amounts.The trained network can discover hidden data patterns in unseen data.The learning algorithm and seed selection play a vital role in the performance of the network.The seed selection is done using the spectral indices to further improve the performance of the network.The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays.It emulates three unique foraging behaviours namelys chain,cyclone,and somersault foraging.The satellite images contain enormous amount of data and thus require exploration in large search space.The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively.The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager(OLI)images of New Brunswick area.The method was applied to identify and classify the land cover changes in the area induced by flooding.The images are classified using the proposed method and a change map is developed using post classification comparison.The change map shows that a large amount of agricultural area was washed away due to flooding.The measurement of the affected area in square kilometres is also performed for mitigation activities.The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased.The performance of the proposed method is done with existing state-of-the-art methods.展开更多
This work deals with quantitative analysis of multicomponent mud logging gas based on infrared spectra. An accurate analysis method is proposed by combining a genetic algorithm(GA) and a radial basis function neural n...This work deals with quantitative analysis of multicomponent mud logging gas based on infrared spectra. An accurate analysis method is proposed by combining a genetic algorithm(GA) and a radial basis function neural network(RBFNN).The GA is used to screen the infrared spectrum of the mixed gas, while the selected spectral region is used as the input of the RBFNN to establish a calibration model to quantitatively analyze the components of logging gas. The analysis results demonstrate that the proposed GA-RBFNN performs better than FS-RBFNN and ES-RBFNN, and our proposed method is feasible.展开更多
To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information crite...To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.展开更多
Compared with the quad-rotor unmanned aerial vehicle (UAV), the coaxial twelve-rotor UAV has stronger load carrying capacity, higher driving ability and stronger damage resistance. This paper focuses on its robust ada...Compared with the quad-rotor unmanned aerial vehicle (UAV), the coaxial twelve-rotor UAV has stronger load carrying capacity, higher driving ability and stronger damage resistance. This paper focuses on its robust adaptive control. First, a mathematical model of a coaxial twelve-rotor is established. Aiming at the problem of model uncertainty and external disturbance of the coaxial twelve-rotor UAV, the attitude controller is innovatively adopted with the combination of a backstepping sliding mode controller (BSMC) and an adaptive radial basis function neural network (RBFNN). The BSMC combines the advantages of backstepping control and sliding mode control, which has a simple design process and strong robustness. The RBFNN as an uncertain observer, can effectively estimate the total uncertainty. Then the stability of the twelve-rotor UAV control system is proved by Lyapunov stability theorem. Finally, it is proved that the robust adaptive control strategy presented in this paper can overcome model uncertainty and external disturbance effectively through numerical simulation and prototype of twelve-rotor UAV tests.展开更多
自然界风速的多变性与风机变桨系统的迟缓性会导致风机输出功率的不稳定。为了改善风机输出功率的稳定,首先基于RBF神经网络RBFNN(radial basis function neural network),以功率差作为信号来源,设计了RBF-PID自适应变桨控制器,建立了...自然界风速的多变性与风机变桨系统的迟缓性会导致风机输出功率的不稳定。为了改善风机输出功率的稳定,首先基于RBF神经网络RBFNN(radial basis function neural network),以功率差作为信号来源,设计了RBF-PID自适应变桨控制器,建立了风力机及变桨距机构仿真模型。其次,建立了2种风况模型,较好地模拟了自然界基本风况。仿真表明:在不同风况下对比常规模糊控制与PID控制,RBF-PID参数自适应方法在风速波动较大的情况下能够更好地稳定输出功率,且减小了变桨的幅值与频率,增加了风机的寿命。展开更多
基金supported by the National Natural Science Foundation of China(61890930-5,61533002,61603012)the Major Science and Technology Program for Water Pollution Control and Treatment of China(2018ZX07111005)+1 种基金the National Key Research and Development Project(2018YFC1900800-5)Beijing Municipal Education Commission Foundation(KM201710005025)
文摘Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm(DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly,the information processing strength(IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm(DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIASORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.
基金supported in part by the National Natural Science Foundation of China(61433004,61703289)
文摘For a single machine infinite power system with thyristor controlled series compensation(TCSC) device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network(RBFNN). The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error,which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function,which can guarantee the uniform ultimate boundedness(UUB) of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.
基金supported partly by the National Natural Science Foundation of China(No.62103440)partly by the National Program on Key Basic Research Project,China(No.2015CB755800).
文摘Flight risk prediction is significant in improving the flight crew's situational awareness because it allows them to adopt appropriate operation strategies to prevent risk expansion caused by abnormal conditions,especially aircraft icing conditions.The flight risk space representing the nonlinear mapping relations between risk degree and the three-dimensional commanded vector(commanded airspeed,commanded bank angle,and commanded vertical velocity)is developed to provide the crew with practical risk information.However,the construction of flight risk space by means of computational flight dynamics suffers from certain defects,including slow computing speed.Accordingly,an intelligent approach for flight risk prediction is proposed to address these defects based on neural networks.Radial Basis Function Neural Network(RBFNN)is optimized using Adaptive Particle Swarm Optimization(APSO).To optimize both the parameters and the structure of APSO-RBFNN,a fitness function containing the training accuracy and network structure size is proposed.Extensive experimental results demonstrate that the flight risk predicted by APSO-RBFNN is very close to that obtained via computational flight dynamics.The average error(RMSE)is less than 10^(-1).The approach achieves a speedup close to 1000x compared with computational flight dynamics.In addition,some flight upset and recovery cases are presented to illustrate the efficiency of the intelligent approach for flight risk prediction.
文摘A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data.Thus,in this paper,a Radial Basis Function Neural Network(RBFNN)trained using Manta Ray Foraging Optimization algorithm(MRFO)is proposed.RBFNN is a three-layer network comprising of input,output,and hidden layers that can process large amounts.The trained network can discover hidden data patterns in unseen data.The learning algorithm and seed selection play a vital role in the performance of the network.The seed selection is done using the spectral indices to further improve the performance of the network.The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays.It emulates three unique foraging behaviours namelys chain,cyclone,and somersault foraging.The satellite images contain enormous amount of data and thus require exploration in large search space.The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively.The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager(OLI)images of New Brunswick area.The method was applied to identify and classify the land cover changes in the area induced by flooding.The images are classified using the proposed method and a change map is developed using post classification comparison.The change map shows that a large amount of agricultural area was washed away due to flooding.The measurement of the affected area in square kilometres is also performed for mitigation activities.The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased.The performance of the proposed method is done with existing state-of-the-art methods.
基金supported by the Natural Science Foundation of Tianjin(Nos.16JCQNJC02100,15JCYBJC51700 and 16JCYBJC15400)the National Key Scientific Instrument and Equipment Development Project of China(No.2012YQ0901670602)+1 种基金the State Key Laboratory of Precision Measuring Technology and Instruments(Tianjin University,No.PIL1603)the Program for Innovative Research Team in University of Tianjin(No.TD13-5036)
文摘This work deals with quantitative analysis of multicomponent mud logging gas based on infrared spectra. An accurate analysis method is proposed by combining a genetic algorithm(GA) and a radial basis function neural network(RBFNN).The GA is used to screen the infrared spectrum of the mixed gas, while the selected spectral region is used as the input of the RBFNN to establish a calibration model to quantitatively analyze the components of logging gas. The analysis results demonstrate that the proposed GA-RBFNN performs better than FS-RBFNN and ES-RBFNN, and our proposed method is feasible.
基金Project(41374118)supported by the National Natural Science Foundation,ChinaProject(20120162110015)supported by Research Fund for the Doctoral Program of Higher Education,China+3 种基金Project(2015M580700)supported by the China Postdoctoral Science Foundation,ChinaProject(2016JJ3086)supported by the Hunan Provincial Natural Science Foundation,ChinaProject(2015JC3067)supported by the Hunan Provincial Science and Technology Program,ChinaProject(15B138)supported by the Scientific Research Fund of Hunan Provincial Education Department,China
文摘To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.
基金Supported by the National Natural Science Foundation of China(No.11372309,61304017)Youth Innovation Promotion Association(No.2014192)+1 种基金the Provincial Special Funds Project of Science and Technology Cooperation(No.2017SYHZ0024)the Key Technology Development Project of Jilin Province(No.20150204074GX)
文摘Compared with the quad-rotor unmanned aerial vehicle (UAV), the coaxial twelve-rotor UAV has stronger load carrying capacity, higher driving ability and stronger damage resistance. This paper focuses on its robust adaptive control. First, a mathematical model of a coaxial twelve-rotor is established. Aiming at the problem of model uncertainty and external disturbance of the coaxial twelve-rotor UAV, the attitude controller is innovatively adopted with the combination of a backstepping sliding mode controller (BSMC) and an adaptive radial basis function neural network (RBFNN). The BSMC combines the advantages of backstepping control and sliding mode control, which has a simple design process and strong robustness. The RBFNN as an uncertain observer, can effectively estimate the total uncertainty. Then the stability of the twelve-rotor UAV control system is proved by Lyapunov stability theorem. Finally, it is proved that the robust adaptive control strategy presented in this paper can overcome model uncertainty and external disturbance effectively through numerical simulation and prototype of twelve-rotor UAV tests.
基金supported by National Outstanding Youth Science Foundation(61125306)National Natural Science Foundation of Major Research Plan(91016004,61034002)+2 种基金Specialized Research Fund for the Doctoral Program of Higher Education of China(20110092110020)Open Fund of Key Laboratory of Measurement and Control of Complex Systems of Engineering(Southeast University)Ministry of Education(MCCSE2013B01)
文摘自然界风速的多变性与风机变桨系统的迟缓性会导致风机输出功率的不稳定。为了改善风机输出功率的稳定,首先基于RBF神经网络RBFNN(radial basis function neural network),以功率差作为信号来源,设计了RBF-PID自适应变桨控制器,建立了风力机及变桨距机构仿真模型。其次,建立了2种风况模型,较好地模拟了自然界基本风况。仿真表明:在不同风况下对比常规模糊控制与PID控制,RBF-PID参数自适应方法在风速波动较大的情况下能够更好地稳定输出功率,且减小了变桨的幅值与频率,增加了风机的寿命。
