phaselockedloopdd

A detail Matlab simulation code example for BFSK performance

粒子群算法工具包，包括动态环境的寻优，还可以直观看到粒子的搜索过程(Particle swarm optimization tool kit, including the dynamic environment of the optimization, you can also see the particles in an intuitive search process)

有源电力滤波器在产生指令电流时，基于瞬时无功功率的检测方法需要用到低通滤波器，本程序能够实现滑动滤波！(APF generating command current, based on instantaneous reactive power detection methods need to use a low-pass filter, the program can achieve sliding filter!)

说明：  自适应滤波和仿真实验，有毕设的同学可以参考下(Adaptive filtering and simulation experiments, there is complete set up for students to refer to the following)

产生CA码的程序，利用扩频码的性质产生GOLD码，最后形成CA码(CA code generating)

MUSIC算法是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。MUSIC算法就是利用这两个互补空间之间的正交特性来估计空间信号的方位。噪声子空间的所有向量被用来构造谱，所有空间方位谱中的峰值位置对应信号的来波方位。MUSIC算法大大提高了测向分辨率，同时适应于任意形状的天线阵列，但是原型MUSIC算法要求来波信号是不相干的。(MUSIC algorithm is a matrix decomposition method based on feature space. From the geometric point of view, the observed spatial signal processing can be decomposed into signal subspace and noise subspace, it is clear that the two spaces are orthogonal. The signal received by the array to the subspace of the covariance matrix of the signal component corresponding eigenvectors, the noise subspace of the covariance matrix of all by the smallest eigenvalue (noise variance) eigenvectors corresponding to the composition. MUSIC algorithm is the use of orthogonal properties between these two complementary space to estimate the spatial orientation of the signal. Noise subspace of all vectors are used to construct the spectrum, all the spatial orientation of the spectrum corresponding to the peak position of the signal wave direction. MUSIC algorithm greatly improves the resolution measurements, while the antenna array adapted to any shape, but the prototype MUSIC algorithm requires to wave sign)

Search in Red-Black Tree

determining the coefficients of the reaction against a system by the method of genetic algorithms

There are 19 questions and 1 programming exercise. Some of them are very easy, some of them are really hard and challenging, and these will be marked as such. If you don’t want to be challenged too much, you can skip the hard questions

实验中使用随机反向传播算法对构造的神经网络进行学习，最终得到构造的神经网络的权值矩阵。(Experiment using the random back-propagation algorithm to construct the neural network learning, the final structure of the neural network obtained the weight matrix.)