power-system-state-estimator

y_compare= 1.0954 1.1832 1.2649 1.3416 1.4142 1.4833 1.5492 1.6125 1.6734 1.7321 1.0954 1.1832 1.2649 1.3416 1.4142 1.4832 1.5492 1.6125 1.6733 1.7321 var_y=2.1742e-010 精度再上一个层次(三阶代数精度) (y_compare = 1.0954 1.1832 1.2649 1.3416 1. 4142 1.4833 1.5492 1.6125 1.6734 1.7321 1.0954 1.1832 1.2649 1.3416 1.4142 1.4832 1.5492 1.61 25 1.6733 1.7321 var_y = 2.1742e-010 precision on a level again ( Third-order algebraic precision))

编写针对一个搜索点宏块比较的下面两个匹配函数：均方误差(MSE)、绝对值误差(MAD)。函数名直接使用简写。要求当前帧和参考帧的数据均采用二维数组表示。(Writing for a search point macroblock compare the following two matching functions: the mean square error (MSE), absolute error (MAD). The function name directly using shorthand. Requirements of the current frame and reference frame data are used two-dimensional array.)

Finding roots with the secant methods

自编 包括动态时间校准算法 求snr算法 及其子程序(Including dynamic time self-calibration algorithm for SNR algorithm and its subroutine)

对二进制序列进行APSK调制，绘出调制后的波形(APSK binary sequence modulation, the modulated waveform drawn)

MIMO Source number detection radii

在源信号和传输信道未知情况下，只利用接收天线的观测数据抽取源信号，称为盲信号分离.盲信号分离不仅是信号处理界、而且也是神经网络界的研究热点课题，在无线数据通信、雷达、图像、语音、医学以及地震信号处理等领域都具有广阔的应用前景.采用自然梯度法和分阶段学习法。(The source signal and transmission channel is unknown circumstances, using only observational data extraction receiving antenna source signal, known as blind signal separation. Blind signal separation is not only the signal processing community, but also a neural network hot topic in research circles, in the wireless data communications , radar images, audio, medical and seismic signal processing and other fields have broad application prospects. and the phases using the natural gradient learning method.)

说明：  数值分析-函数逼近-插值法-lagrange插值法的实现(- Function approximation- interpolation-lagrange interpolation achieve numerical analysis)

decoder of cellular/telecom

对一个具有150组80个元素的二进制输入矢量P，采用simal.m脚本文件进行自动分类的典型应用程序，其中取r=0.75，竞争层初始节点取10个，对ART1网络进行训练(Has 150 for a group of binary input vector P 80 elements, using simal. M script file for automatic classification of typical applications, including r = 0.75, the competitive layer take 10, the initial node of ART1 network for training )