To counter the defect of traditional genetic algorithms, an improved adaptivegenetic algorithm with the criterion of premature convergence is provided. The occurrence ofpremature convergence is forecasted using colony...To counter the defect of traditional genetic algorithms, an improved adaptivegenetic algorithm with the criterion of premature convergence is provided. The occurrence ofpremature convergence is forecasted using colony entropy and colony variance. When prematureconvergence occurs, new individuals are generated in proper scale randomly based on superiorindividuals in the colony. We use these new individuals to replace some individuals in the oldcolony. The updated individuals account for 30 percent - 40 percent of all individuals and the sizeof scale is related to the distribution of the extreme value of the target function. Simulationtests show that there is much improvement in the speed of convergence and the probability of globalconvergence.展开更多
This paper explores the convergence of a class of optimally conditioned self scaling variable metric (OCSSVM) methods for unconstrained optimization. We show that this class of methods with Wolfe line search are glob...This paper explores the convergence of a class of optimally conditioned self scaling variable metric (OCSSVM) methods for unconstrained optimization. We show that this class of methods with Wolfe line search are globally convergent for general convex functions.展开更多
The paper studies the convergence and the superconvergence of the biquadratic finite element for Poisson＇ problem on anisotropic meshes. By detailed analysis, it shows that the biquadratic finite element is anisotrop...The paper studies the convergence and the superconvergence of the biquadratic finite element for Poisson＇ problem on anisotropic meshes. By detailed analysis, it shows that the biquadratic finite element is anisotropically superconvergent at four Gauss points in the element. Key words：展开更多
This paper introduces several algorithms for signal estimation using binary-valued outputsensing.The main idea is derived from the empirical measure approach for quantized identification,which has been shown to be con...This paper introduces several algorithms for signal estimation using binary-valued outputsensing.The main idea is derived from the empirical measure approach for quantized identification,which has been shown to be convergent and asymptotically efficient when the unknown parametersare constants.Signal estimation under binary-valued observations must take into consideration oftime varying variables.Typical empirical measure based algorithms are modified with exponentialweighting and threshold adaptation to accommodate time-varying natures of the signals.Without anyinformation on signal generators,the authors establish estimation algorithms,interaction between noisereduction by averaging and signal tracking,convergence rates,and asymptotic efficiency.A thresholdadaptation algorithm is introduced.Its convergence and convergence rates are analyzed by using theODE method for stochastic approximation problems.展开更多
基金The Natural Science Foundation of Jiangsu Province (BK99011).
文摘To counter the defect of traditional genetic algorithms, an improved adaptivegenetic algorithm with the criterion of premature convergence is provided. The occurrence ofpremature convergence is forecasted using colony entropy and colony variance. When prematureconvergence occurs, new individuals are generated in proper scale randomly based on superiorindividuals in the colony. We use these new individuals to replace some individuals in the oldcolony. The updated individuals account for 30 percent - 40 percent of all individuals and the sizeof scale is related to the distribution of the extreme value of the target function. Simulationtests show that there is much improvement in the speed of convergence and the probability of globalconvergence.
文摘This paper explores the convergence of a class of optimally conditioned self scaling variable metric (OCSSVM) methods for unconstrained optimization. We show that this class of methods with Wolfe line search are globally convergent for general convex functions.
基金the Henan Natural Science Foundation(072300410320)the Foundation Study of the Education Department of Henan Province(200510460311)
文摘The paper studies the convergence and the superconvergence of the biquadratic finite element for Poisson＇ problem on anisotropic meshes. By detailed analysis, it shows that the biquadratic finite element is anisotropically superconvergent at four Gauss points in the element. Key words：
基金supported in part by the National Science Foundation under ECS-0329597 and DMS-0624849in part by the Air Force Office of Scientific Research under FA9550-10-1-0210+2 种基金supported by the National Science Foundation under DMS-0907753 and DMS-0624849in part by the Air Force Office of Scientific Research under FA9550-10-1-0210supported in part by a research grant from the Australian Research Council
文摘This paper introduces several algorithms for signal estimation using binary-valued outputsensing.The main idea is derived from the empirical measure approach for quantized identification,which has been shown to be convergent and asymptotically efficient when the unknown parametersare constants.Signal estimation under binary-valued observations must take into consideration oftime varying variables.Typical empirical measure based algorithms are modified with exponentialweighting and threshold adaptation to accommodate time-varying natures of the signals.Without anyinformation on signal generators,the authors establish estimation algorithms,interaction between noisereduction by averaging and signal tracking,convergence rates,and asymptotic efficiency.A thresholdadaptation algorithm is introduced.Its convergence and convergence rates are analyzed by using theODE method for stochastic approximation problems.
