DS1643

matlab编的数独游戏，挺好玩的，大家一起研究一下(Sudoku)

8 Phase Shift Keying modulation

添加applet/application(Add applet/application)

灰色关联度分析，只需要直接输入数据即可用(grey relational analysis)

Matlab p file for converting a binary file to an 8-connected boundary.

说明：  本书介绍了Matlab在一些经典算法上的应用，以及详细讲解了图形界面（GUI）的用法。(This book describes some of the classic algorithms in Matlab applications, and gave a detailed graphical user interface (GUI) usage.)

用matlab统计不同距离的RSSI值，画折线图，并最将各距离处平均值用最小二乘法拟合成3到5项多项式曲线(RSSI value of the matlab statistics at different distances, draw a line chart, and most average each distance with the least square method were fitted polynomial curve of 3-5)

等间距线性阵列，阵元数M=8，阵元间距d= λ /2，等幅加权，目标方位φr =0°。产生空间白噪声矢量（复高斯分布）。 设阵列信号矢量元素s(m)=exp[j mψr]，其信号幅度为1，该阵元接收机附加的高斯噪声为n(m)=nmr+jnmi，其中实虚部均为独立同分布N(0, σ2)的高斯随机数，则该通道合成信号x(m)=s(m) + n(m)，其中信噪比为： SNR = 10 lg[1/(2σ2)] = – 3 – 10 lg(σ2) (dB) (The spacing of the linear array, and the array element number M = 8, the array element spacing d = λ/2, equal amplitude weighted target azimuth φR = 0. Space generated white noise vector (complex Gaussian distribution). Let array signal vector elements s (m) = exp [j mψr], the signal amplitude, the element receiver additional Gaussian noise n (m) = nmr+jnmi which the real and imaginary parts are independent and identically distributed N ( Gaussian random numbers of 0, σ2), the channel the synthesis signal x (m) = s (m) the PPLS n (m), where SNR is: SNR = 10 lg [1/(2σ2)] =- 3- 10 lg (σ2) (dB))

基于最小二乘支持向量机理论,结合某风电场实测风速数据,建立了最小二乘支持向量机风速预测模型。对该风电场的风速进行了提前1h的预测,其预测的平均绝对百分比误差仅为8.55 ,预测效果比较理想。同时将文中的风速预测模型与神经网络理论、支持向量机(support vector machine,SVM)理论建立的风速预测模型进行了比较。仿真结果表明,文中所提模型在预测精度和运算速度上皆优于其他模型。 (Based on least squares support vector machine theory, combined with a wind farm measured wind speed data, the establishment of a wind vector machine forecasting model of least squares support. The velocity of the wind farm were predicted in advance 1h, the mean absolute percentage error of only 8.55 of its forecast, forecast effect is ideal. While the text of the wind speed forecasting models and neural networks, support vector machines (support vector machine, SVM) wind speed prediction models were compared with established theories. Simulation results show that our proposed model on prediction accuracy and computing speed are superior to other models.)

牛顿拉夫逊法计算配电网潮流的程序，适用于大多数的网络，具有参考性( U725B u983.0 u622B u9279 u6A0B u5E0B u5E8F uFF0C u9002 u7528 u4E8E u5927 u591A u6570 u7684 u7L5 U7EDC uFF0C u5177 u6709 u53C2 u8003 u6027)