We propose a modified evolutionary computation method to solve the optimization problem of additively decomposed function with constraints. It is based on factorized distribution instead of penalty function and any tr...We propose a modified evolutionary computation method to solve the optimization problem of additively decomposed function with constraints. It is based on factorized distribution instead of penalty function and any transformation to a linear model or others. The feasibility and convergence of the new algorithm are given. The numerical results show that the new algorithm gives a satisfactory performance.展开更多
General neural network inverse adaptive controller has two flaws: the first is the slow convergence speed; the second is the invalidation to the non-minimum phase system. These defects limit the scope in which the neu...General neural network inverse adaptive controller has two flaws: the first is the slow convergence speed; the second is the invalidation to the non-minimum phase system. These defects limit the scope in which the neural network inverse adaptive controller is used. We employ Davidon least squares in training the multi-layer feedforward neural network used in approximating the inverse model of plant to expedite the convergence, and then through constructing the pseudo-plant, a neural network inverse adaptive controller is put forward which is still effective to the nonlinear non-minimum phase system. The simulation results show the validity of this scheme.展开更多
Speckle filtering of synthetic aperture radar (SAR) images while preserving the spatial signal variability (texture and fine structures) still remains a challenge. Many algorithms have been proposed for the SAR imager...Speckle filtering of synthetic aperture radar (SAR) images while preserving the spatial signal variability (texture and fine structures) still remains a challenge. Many algorithms have been proposed for the SAR imagery despeckling. However, simulated annealing (SA) methods is one of excellent choices currently. A critical problem in the study on SA is to provide appropriate cooling schedules that ensure fast convergence to near-optimal solutions. This paper gives a new necessary and sufficient condition for the cooling schedule so that the algorithm state converges in all probability to the set of global minimum cost states. Moreover, it constructs an appropriate objective function for SAR image despeckling. An experimental result of the actual SAR image processing is obtained.展开更多
基金National Natural Science Foundation of China(60072029)
文摘We propose a modified evolutionary computation method to solve the optimization problem of additively decomposed function with constraints. It is based on factorized distribution instead of penalty function and any transformation to a linear model or others. The feasibility and convergence of the new algorithm are given. The numerical results show that the new algorithm gives a satisfactory performance.
基金Tianjin Natural Science Foundation !983602011National 863/CIMS Research Foundation !863-511-945-010
文摘General neural network inverse adaptive controller has two flaws: the first is the slow convergence speed; the second is the invalidation to the non-minimum phase system. These defects limit the scope in which the neural network inverse adaptive controller is used. We employ Davidon least squares in training the multi-layer feedforward neural network used in approximating the inverse model of plant to expedite the convergence, and then through constructing the pseudo-plant, a neural network inverse adaptive controller is put forward which is still effective to the nonlinear non-minimum phase system. The simulation results show the validity of this scheme.
基金ThisprojectwassupportedbytheNationalNaturalScienceFoundationofChina (No .6 98310 40 )
文摘Speckle filtering of synthetic aperture radar (SAR) images while preserving the spatial signal variability (texture and fine structures) still remains a challenge. Many algorithms have been proposed for the SAR imagery despeckling. However, simulated annealing (SA) methods is one of excellent choices currently. A critical problem in the study on SA is to provide appropriate cooling schedules that ensure fast convergence to near-optimal solutions. This paper gives a new necessary and sufficient condition for the cooling schedule so that the algorithm state converges in all probability to the set of global minimum cost states. Moreover, it constructs an appropriate objective function for SAR image despeckling. An experimental result of the actual SAR image processing is obtained.
