advancedPIDMATLAB

说明：  fuzzy inertia weight particle swarm optimization

It is great model of one part of engine diesel model. This particular file contains model of intake manifold.

ML gradient descend ，Stanford course(machine learning ，Andrew Ng)

this code to enhanc grayscale images

matlab最优化程序包括 无约束一维极值问题 进退法 黄金分割法 斐波那契法 牛顿法基本牛顿法 全局牛顿法 割线法 抛物线法 三次插值法 可接受搜索法 Goidstein法 Wolfe.Powell法 单纯形搜索法 Powell法 最速下降法 共轭梯度法 牛顿法 修正牛顿法 拟牛顿法 信赖域法 显式最速下降法， Rosen梯度投影法 罚函数法 外点罚函数法 內点罚函数法 混合罚函数法 乘子法 G－N法 修正G－N法 L－M法 线性规划 单纯形法 修正单纯形法 大M法 变量有界单纯形法 整数规划 割平面法 分支定界法 0-1规划 二次规划 拉格朗曰法 起作用集算法 路径跟踪法 粒子群优化算法 基本粒子群算法 带压缩因子的粒子群算法 权重改进的粒子群算法 线性递减权重法 自适应权重法 随机权重法 变学习因子的粒子群算法 同步变化的学习因子 异步变化的学习因子 二阶粒子群算法 二阶振荡粒子群算法 (matlab optimization process includes Non-binding one-dimensional extremum problems Advance and retreat method Golden Section Fibonacci method of basic Newton s method Newton s method Newton s Law of the global secant method parabola method acceptable to the three interpolation search method   Goidstein France Wolfe.Powell France Simplex search method Powell steepest descent method Conjugate gradient method Newton s method Newton s method to amend Quasi-Newton Method trust region method explicitly steepest descent method, Rosen gradient projection method Penalty function method outside the penalty function method within the penalty function method Mixed penalty function multiplier method   G-N was amended in G-N method L-M method Of linear programming simplex method, revised simplex method Big M method variables bounded simplex method, Cutting Plane Method integer programming branch and bound method 0-1 programming quadratic programming )

recognizing fingerprint image

matlab program for calculating fractal dimension using boxcount algorithm

在MATLAB中用小波变换实现边缘检测，具有一定的参考价值。(Wavelet transform used in MATLAB to achieve edge detection, has a certain reference value.)

用MATLAB语言编写的pade近似程序，本人保留版权(the analysis of pade)

These files contain all the code necessary to run the example in the Webinar "Signal Processing for Machine Learning in MATLAB". They also include code to automate the download and preparation of the dataset used. In that webinar we presented an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone. We used consolidated signal processing methods to extract a fairly small number of highly-descriptive features, and finally trained a small Neural Network to map the feature vectors into the 6 different activity classes of a pre-recorded dataset. The topics discussed include: * Signal manipulation and visualisation * Design and application of digital filters * Frequency-domain analysis * Automatic peak detection * Feature extraction from signals * Train and test of simple Neural Networks