solar-battery

使用MATLAB软件对智能天线进行DOA估计的MUSIC算法仿真研究，希望对大家学习有帮助(Using MATLAB software on smart antennas for DOA Estimation Algorithm for Simulation of MUSIC, in the hope that will help them to learn)

说明：  用于信号滤波，要事先给出滤波器系数，给出心电加上正弦干扰信号的滤波过程。(For signal filtering, to filter coefficients is given in advance, given ECG+ sinusoidal interference signal filtering process.)

双极性pwm控制技术逆变器，采用pwm技术对逆变器进行控制实现逆变功能(pwm nibianqi)

说明：  台湾大学的关于EM算法的讲义，讲述的非常清楚，受益匪浅，特拿出来分享(Taiwan University, lectures on the EM algorithm, described in very clear benefit, particularly to share out)

使用马尔科夫链蒙特卡洛法的一个莫诺模型例子(A Monod model example of the use of Markov chain Monte Carlo method)

构造模拟信号进行ICA独立分量盲源分离程序，值得参考和下载哦，可直接运行(Constructed analog signal the ICA independent component blind source separation program, worth considering and download Oh, and can be run directly)

里面是一个地震射线追踪的源码程序，还有相关的文档，适合学习地球物理专业的学生。(Inside is a seismic source ray tracing program, as well as related documentation for learning geophysics majors.)

汽车理论中二自由度操纵稳定性模型，在MATLAB中进行SIMULINK仿真，得到方向盘阶跃输入下的横摆角速度响应.(Vehicle handling stability theory of two degree of freedom model, in the MATLAB SIMULINK simulation, get the wheel under the step input of horizontal pendulum angular velocity response.)

永磁同步电机的直接转矩控制，控制效果很好！通过磁链和转矩估计，达到对转矩的直接控制。(Direct torque control of permanent magnet synchronous motor, the control effect is very good!Estimated by the flux and torque to achieve the direct control of the torque.)

NP是美国匹兹堡大学的T.L.Saaty 教授于1996年提出了一种适应非独立的递阶层次结构的决策方法，它是在网络分析法（AHP）基础上发展而形成的一种新的实用决策方法。其关键步骤有以下几个： 1 确定因素，并建立网络层和控制层模型。 2 创建比较矩阵。 3 按照指标类型针对每列进行规范化。 4 求出每个比较矩阵的最大特征值和对应的特征向量。 5 一致性检验。如果不满足，则调整相应的比较矩阵中的元素。 6 将各个特征向量单位化(归一化)，组成判断矩阵。 7 将控制层的判断矩阵和网络层的判断矩阵相乘，得到加权超矩阵。 8 将加权超矩阵单位化(归一化)，求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。 (NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following: A determining factor, and a network layer and control layer model. 2 create a comparison matrix. For each of the three types of indicators in accordance with normalized columns. 4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors. 5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements. 6 will each feature vector units of (normalized), to determine the composition of matrix. 7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix. 8 of the weighted super-matrix units of (normalized), seeking the powe)