Based on the single scaling wavelet frame theory and radial basis function neural network, a multi dimensional input and output wavelet network is constructed.
在探索单尺度径向小波框架与径向基函数网络对函数逼近特性相似的基础上，构造了单尺度径向基小波网络。
The system includes the following nine modules: pretreatment, wavelet analysis, spectral analysis, forward modeling, inversion, input, output, graph display and help.
系统包括预处理、小波分析、频谱分析、正演、反演、输入、输出、图形显示、帮助等九个模块。
The results show that this algorithm can model input and output learning kernel of dynamic nonlinear system quickly, which is superior to other learning methods of wavelet network.
结果表明该算法能够对动态非线性系统的输入输出快速学习和建模，优于其它小波网络的学习算法。
After the clustering characteristics of image wavelet coefficients' significance distribution in each sub-band was analyzed, run-length coding (RLC) was introduced into SPIHT's output stage.
针对图像小波系数在各子带内显著性分布的聚簇特征，提出了在SPIHT算法的输出环节引入游程编码。
The good localization characteristics of wavelet functions in both time and frequency space allowed hierarchical multi-resolution learning of input-output data mapping.
利用小波变换所具有的良好的时频分析特性，实现了输入输出之间映射关系的多分辨学习。
The effects of wavelet transform are studied in detail. The amount of output correlation channels is estimated, and methods to improve parallelism of the system are provided.
文中深入分析了引入子波变换对体全息相关器所产生的作用，给出了系统一次并行输出相关通道数目的估算方法及提高系统并行性能的主要途径。
The wavelet transform filter and infinite impulse response (IIR) digital filter is applied, and the output signals of the three filters are compared.
此外，本文还对陀螺信号进行了小波变换滤波和IIR数字滤波器滤波并对上述三种方法的滤波性能进行了对比分析。
In order to accurately distinguish the origins bringing about abrupt changes in sensor output, a method is presented for wavelet frequency analysis based on mathematical model.
为了准确区分传感器突变信号产生的原因，提出了基于数学模型的小波频带分析法。
The good localization characteristics of wavelet functions in both time and frequency space allow hierarchical multi-resolution learning of input-output data mapping.
由于小波变换在时间和频率空间具有良好的定位特性，使小波神经网络可对输入、输出数据进行多分辨的学习训练。
The problem of signal processing in north-finders has been studied. The wavelet-based hidden Markov models (WHMM) are used to denoise the Gyros output signals in continuous rotary north-finders.
研究寻北仪惯性传感器信号处理问题，采用基于小波域的隐马尔可夫模型(WHMM)，对连续旋转式寻北仪陀螺的输出信号进行降噪处理。
The problem of signal processing in north-finders has been studied. The wavelet-based hidden Markov models (WHMM) are used to denoise the Gyros output signals in continuous rotary north-finders.
研究寻北仪惯性传感器信号处理问题，采用基于小波域的隐马尔可夫模型(WHMM)，对连续旋转式寻北仪陀螺的输出信号进行降噪处理。
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