LDA_Test

说明：  基于石油钻井神经网络PID仿真器模型，该模型验证了该算法的可行性和有效性(BTF simulink fangzhen)

阵列信号处理中经典的的ESPRIT算法，用于DOA估计（波达方向估计）(array signal processing classic ESPRIT algorithm for DOA (DOA estimated))

matlab开发环境，．编程实现二维DFT的变换，并与标准函数结果fft2进行比较，基本傅里叶、离散余弦变换，空域（完成两个程序） 增强噪声后再进行处理，利用标准函数，求图像阈值分割、边缘检测的基本使用（lena256.bmp，方法：graythresh、edge），求分水岭，利用标准函数dwt2、idwt2分解图像man_pic.jpg，基本 BP-BPbase1 (matlab development environment. Programming dimensional DFT transform, and with the standard functions compare results fft2 basic Fourier, discrete cosine transform, airspace (to complete the two programs) to enhance the noise and then processed using standard functions for image thresholding, edge Detection of basic use (lena256.bmp, method: graythresh, edge), seeking watershed, using a standard function dwt2, idwt2 decomposed image man_pic.jpg, basic BP-BPbase1)

计算目标和海洋回波的功率谱密度,小波包分析提取振动信号中的特征频率，这是第二能量熵的matlab代码。( Calculating a target and ocean echo power spectral density, Wavelet packet analysis to extract vibration signal characteristic frequency, This is the second energy entropy matlab code.)

说明：  This file includes type-2 fuzzy logic controller and neural network papers.

说明：  混沌随机序列的MATLAB产生源程序，混沌随机序列的MATLAB产生源程序(MATLAB generated chaotic random sequence source, chaotic random sequence generated source MATLAB)

it is an ebook for learning pid controller design with matlab functions

基于粒子群算法分别以车身加速度和车轮动载荷进行悬架参数寻优(optimization of suspension parameters based on particle swarm algorithm )

说明：  bp神经网络预测程序，可用于与小波神经网络对比(BP Neural Network Prediction Program, which can be used to compare with Wavelet Neural Network)

there are some newly released Neural Network Example Programs for Character Recognition, which based on Image Processing Toolbox,Neural Network Toolbox in MATLAB, which is quite informative for the beginners in Neural networks applicators(there are some newly released Neural Network Example Programs for Character Recognition, which based on Image Processing Toolbox, Neural Network Toolbox in MATLAB, which is quite informative for the beginners in Neural networks applicators)