Aimed at the complex flight control system of a fighter,a kind of robust adaptive control methods using multiple models is presented to make the control system track the given signal under different working conditions...Aimed at the complex flight control system of a fighter,a kind of robust adaptive control methods using multiple models is presented to make the control system track the given signal under different working conditions and to reconfigure the control law for some structural failures. Firstly,the multiple-model control structure is formed by several linear models and one fuzzy model. In the fuzzy logic way,weights of the multiple-model adaptive controller are obtained. Then,a dynamic structure adaptive neural network is introduced to stabilize the whole system and eliminate the influence caused by the frequent switching. Simulation results show that the control method is effective by demonstrating the normal flight process and the control simulation with failures.展开更多
In order to investigate the restoration of low resolution images, the linear and nonlinear interpolation methods were applied for the interpolation of the com- mon information matrix obtained from a series of pictures...In order to investigate the restoration of low resolution images, the linear and nonlinear interpolation methods were applied for the interpolation of the com- mon information matrix obtained from a series of pictures, getting the restructuring matrix. The characteristic block with the best restoration effect was determined by analyzing the pixel difference of the common information of each image at the same position. Then the characteristic blocks and their original blocks were used to build and train neural network. Finally, images were restored by the neural network and the differences between pictures were reduced. Experimental results showed that this method could significantly improve the efficiency and precision of algorithm.展开更多
Detection of weak underwater signals is an area of general interest in marine engineering.A weak signal detection scheme was developed; it combined nonlinear dynamical reconstruction techniques, radial basis function ...Detection of weak underwater signals is an area of general interest in marine engineering.A weak signal detection scheme was developed; it combined nonlinear dynamical reconstruction techniques, radial basis function (RBF) neural networks and an extended Kalman filter (EKF).In this method chaos theory was used to model background noise.Noise was predicted by phase space reconstruction techniques and RBF neural networks in a synergistic manner.In the absence of a signal, prediction error stayed low and became relatively large when the input contained a signal.EKF was used to improve the convergence rate of the RBF neural network.Application of the scheme to different experimental data sets showed that the algorithm can detect signals hidden in strong noise even when the signal-to-noise ratio (SNR) is less than -40d B.展开更多
基金Supported by the National Natural Science Foundation of China (60234010)the Aviation ScienceFoundation of China (05E52031)~~
文摘Aimed at the complex flight control system of a fighter,a kind of robust adaptive control methods using multiple models is presented to make the control system track the given signal under different working conditions and to reconfigure the control law for some structural failures. Firstly,the multiple-model control structure is formed by several linear models and one fuzzy model. In the fuzzy logic way,weights of the multiple-model adaptive controller are obtained. Then,a dynamic structure adaptive neural network is introduced to stabilize the whole system and eliminate the influence caused by the frequent switching. Simulation results show that the control method is effective by demonstrating the normal flight process and the control simulation with failures.
基金Supported by the Youth Fund for Science and Technology Research of Institution of Higher Education in Hebei Province in 2016(QN2016243)~~
文摘In order to investigate the restoration of low resolution images, the linear and nonlinear interpolation methods were applied for the interpolation of the com- mon information matrix obtained from a series of pictures, getting the restructuring matrix. The characteristic block with the best restoration effect was determined by analyzing the pixel difference of the common information of each image at the same position. Then the characteristic blocks and their original blocks were used to build and train neural network. Finally, images were restored by the neural network and the differences between pictures were reduced. Experimental results showed that this method could significantly improve the efficiency and precision of algorithm.
基金Supported by China Postdoctoral Science Foundation No.20080441183
文摘Detection of weak underwater signals is an area of general interest in marine engineering.A weak signal detection scheme was developed; it combined nonlinear dynamical reconstruction techniques, radial basis function (RBF) neural networks and an extended Kalman filter (EKF).In this method chaos theory was used to model background noise.Noise was predicted by phase space reconstruction techniques and RBF neural networks in a synergistic manner.In the absence of a signal, prediction error stayed low and became relatively large when the input contained a signal.EKF was used to improve the convergence rate of the RBF neural network.Application of the scheme to different experimental data sets showed that the algorithm can detect signals hidden in strong noise even when the signal-to-noise ratio (SNR) is less than -40d B.
