cascadethreephase

Bark和ERB滤波器组实现的Matlab源程序(Bark and ERB filter banks realize the Matlab source)

给出了利用matlab程序编写的脉冲星EKF仿真程序，对噪声进行时变分析(pulsars navigation EKF)

WSN LOCALAZATION SIMULATION

OFDM中的关于二进制转十进制器的个人设计的MATLAB的算法，比较经典的 ！(OFDM on the binary switch to the metric system for the design of personal MATLAB algorithm, more classic!)

利用CMAC神经网络实现无限逼近sin函数并计算误差实现误差的可视化(CMAC neural network to achieve unlimited approximation sin function and calculate error error visualization)

卡尔曼滤波的matlab实现。卡尔曼滤波的matlab实现。(Kalman filter matlab implementation.)

this is a method of watermarking. where SVD and DCT both are used to get robust and secure watermarking

快速傅里叶变换的matlab实现，用于对信号进行频谱分析(Fast Fourier transform matlab implementation for spectral analysis of signals)

最终的权值矩阵就是滤波器的系数，包含了阵列信号处理的常见算法，是一种双隐层反向传播神经网络，相控阵天线的方向图（切比雪夫加权），添加噪声处理。( The final weight matrix is ??the filter coefficient, Contains a common array signal processing algorithm, Is a two hidden layer back propagation neural network, Phased array antenna pattern (Chebyshev weights), Add noise processing.)

这是一个关于稀疏高斯过程的matlab源码，可以用于计算测试输入的高斯预测值。( spgp_pred computes the SPGP predictive distribution for a set of test inputs. You need to supply a set of pseudo-inputs or basis vectors for the approximation, and suitable hyperparameters for the covariance. You can use any method you like for finding the pseudo-inputs , with the simplest obviously being a random subset of the data. It is coded for Gaussian covariance function, but you could very easily alter this. It is also fine to use for high dimensional data sets. spgp_lik is the SPGP (negative) marginal likelihood and gradients with respect to pseudo-inputs and hyperparameters. So you can use this if you wish to try to optimize the positioning of pseudo-inputs and find good hyperparameters, before using spgp_pred . I would recommend initializing the pseudo-inputs on a random subset of the data, and initializing the hyperparameters sensibly. Its current limitations are that 1) it is slow and memory intensive for high dimensional data sets 2) it is heavi)