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On Efficiency of Simultaneous Perturbation Stochastic Approximation Method for Implementation of an Adaptive Filter

On Efficiency of Simultaneous Perturbation Stochastic Approximation Method for Implementation of an Adaptive Filter
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摘要 In this paper, the simultaneous perturbation stochastic approximation (SPSA) algorithm is used for seeking optimal parameters in an adaptive filter developed for assimilating observations in the very high dimensional dynamical systems. The main results show that the SPSA is capable of yielding the high filter performance similar to that produced by classical optimization algorithms, with better performance for non-linear filtering problems as more and more observations are assimilated. The advantage of the SPSA is that at each iteration it requires only two measurements of the objective function to approximate the gradient vector regardless of the dimension of the control vector (or maximally, three measurements if second-order optimization algorithms are used). The SPSA approach is thus free from the need to develop a discrete adjoint of tangent linear model as it is required up to now for solving optimization problems in very high dimensional systems. This technique offers promising perspectives on developing optimal assimilation systems encountered in the field of data assimilation in meteorology and oceanography.
机构地区 SHO M/HOM
出处 《Computer Technology and Application》 2011年第12期948-962,共15页 计算机技术与应用(英文版)
关键词 Stochastic approximation dominant Schur vectors minimum prediction error adaptive filter stability. 自适应滤波器 随机逼近 逼近方法 扰动 高维动力系统 SPSA 优化算法 过滤性能
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