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Performance comparison among nonparametric probability density estimator radial basis function and adaptive wavelet transform neural networks

机译:非参数概率密度估计器径向基函数与自适应小波变换神经网络的性能比较

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Abstract: Wavelet shrinkage, radial basis function (RBF) have been studied for signal reconstructions. We first use these methods to approximate four specific functions which represent various spatially nonhomogeneous phenomena. Next, we apply these methods to analyze a time series of Paraguay River levels. From the preliminary experiments, we show that wavelet shrinkage was the best estimator. With similar result, secondly came AWTNN and lastly came RBF networks. !15
机译:摘要:对信号重建研究了小波收缩,径向基函数(RBF)。我们首先使用这些方法来近似四种特定功能,该功能代表各种空间非均匀现象。接下来,我们应用这些方法来分析巴拉圭河水位的时间序列。从初步实验中,我们表明小波收缩是最好的估计。具有类似的结果,其次是AWTNN,最后是RBF网络。 !15

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