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This paper reports our preliminary study that aims to examine the effectiveness of training methods for land cover classification by artificial neural networks. We consider three training methods, namely, the gradient descent method, the conjugate gradient method, and the Quasi-Newton method. We apply these methods to derive land cover information from a Landsat Enhanced Thematic Mapper Plus (ETM+)...
In this paper, an adaptive estimation algorithm is proposed for non-linear dynamic systems with unknown static parameters based on combination of particle filtering. The estimates of static parameters are obtained by state samples and maximum-likelihood estimation in particle filtering, and the stochastic approximation is used to approximate the gradient of cost function. The proposed algorithm achieves...
In this paper, an adaptive estimation algorithm is proposed for non-linear dynamic systems with unknown parameters based on combination of particle filtering and SPSA technique. The estimates of parameters are obtained by state samples and maximum-likelihood estimation under particle filtering, and the SPSA is used to approximate the gradient of target function. The proposed algorithm achieves joint...
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