ap_fft

利用双线性变换法，设计一个0.4dB截止频率为10kHz且在30kHz处有最小阻带衰减为50dB的数字巴特沃兹低通滤波器，其抽样率为100kHz。利用教材（4.35）式求解原型模拟滤波器的阶数，并利用Matlab的M文件buttap设计模拟原型滤波器。用M文件bilinear将模拟滤波器传输函数变换成期望的数字传输函数。请用Matlab画出其增益和相位响应，并给出设计的具体步骤。(fuck!!!)

protel元件库，有许多常用与不多见的元件的原理图(Protel libraries, there are many common and rare components of the schematic diagram)

localization error for wireless sensor network by Matlab

对测试信号进行emd 分解，计算其在各个频带中的能量分布(The EMD of the test signal is decomposed, and the energy distribution in each frequency band is calculated)

说明：  实指数序列a=1/2 实指数序列a=1/2(Real exponential sequence a = 1/2)

基于MATLAB的GUI滤波器设计资料。(Based on MATLAB the GUI filter design information.)

梯度下降法是一個一階最佳化算法，通常也稱為最速下降法。 要使用梯度下降法找到一個函數的局部極小值，必須向函數上當前點對應梯度（或者是近似梯度）的反方向的規定步長距離點進行疊代搜索。如果相反地向梯度正方向疊代進行搜索，則會接近函數的局部極大值點；這個過程則被稱為梯度上升法。(The gradient descent method is a first-order optimization algorithm, also commonly referred to as the steepest descent method. To find the local minimum of a function using the gradient descent method, an iterative search must be performed to the specified step distance point in the opposite direction of the gradient (or approximate gradient) of the current point on the function. If the search is reversed in the opposite direction to the positive direction of the gradient, it will approach the local maximum point of the function; this process is called the gradient ascent method.)

这个是直流电机的matlab仿真程序，希望能对大家有所帮助(This is a DC motor matlab simulation program, hoping to help all of you)

包括：用基本粒子群算法求解无约束优化问题，用带压缩因子的粒子群算法求解无约束优化问题，用线性递减权重粒子群优化算法求解无约束优化问题，用自适应权重粒子群优化算法求解无约束优化问题，用随机权重粒子群优化算法求解无约束优化问题，用学习因子同步变化的粒子群优化算法求解无约束优化问题，用学习因子异步变化的粒子群优化算法求解无约束优化问题，用二阶粒子群优化算法求解无约束优化问题，用二阶振荡粒子群优化算法求解无约束优化问题(Include: using the particle swarm algorithm for unconstrained optimization problems, with compression factor of particle swarm algorithm for solving unconstrained optimization problem with linear decreasing weight particle swarm optimization for unconstrained optimization problems, an adaptive weight particle swarm optimization unconstrained optimization problems, random weight particle swarm optimization algorithm for unconstrained optimization problems, with the learning factor synchronous change of particle swarm optimization algorithm for unconstrained optimization problems, with the learning factor asynchronous changes in particle swarm optimization algorithm for solving unconstrained optimization problem , with the second particle swarm optimization for unconstrained optimization problems, with the second-order oscillating particle swarm optimization algorithm for unconstrained optimization problem)

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