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This book presents the MATLAB computer programming system as a problemsolving tool for scientists and engineers who have no prior knowledge of computer programming. It is based on a teach-yourself approach readers are frequently invited to experiment for themselves in order to discover how particular constructs work. The text was originally written as a companion to a hands-on course at the University of Cape Town. Most of the students taking the course had little or no computing experience and came from disadvantaged backgrounds. Consequently the book assumes that readers have no knowledge of computing, unlike most similar books on MATLAB.((2007 Elsevier)Essential Matlab for Engineers and Scientists(3rd Edition))

在matlab中完成直流电测深一维正演，并且实现可视化(1d resisitivity sounding based on matlab)

Simulink for the speed control of indution motor using PWM

时频原子分解算法主要用于建立时频原子库，从而提取出特征信号(Time-frequency atom decomposition algorithm for frequency atoms library created to extract the characteristic signal)

development of an information environment for learning weighting burst into radar

改程序从imu中读取数据，计算出载体的轨迹，速度，姿态，并画出速度与欧拉角(this program takes the data from an IMU as the input and calculates the body s trajectory ,velocity and attitude . it plots the velocities and euler angles vs time and the body s trajectory. )

Adaboost是一种迭代算法，其核心思想是针对同一个训练集训练不同的分类器(弱分类器)，然后把这些弱分类器集合起来，构成一个更强的最终分类器（强分类器）。 load clouds [test_targets, E] = lijsada_boost(patterns, targets, patterns, 100, Stumps ,[]) train_patterns 每列为一样本 train_targets 每列为一样本目标 100 :Number Of Iterations Stumps: Weak Learner Type Learner s parameters :[] adaboost +stumps testRightR = 0.7522(Adaboost is an iterative algorithm)

说明：  模式识别PCA(principle component analysis)源码.matlab 格式。PCA为经典而且经常使用的算法。(pattern recognition PCA (principle component analysis) source. Matlab format. PCA to the classic and often use the algorithm.)

1、经典粒子群算法 2、一次最小二乘法（LS） 3、递推最小二乘算法（RLS） 4、确定性系统的递推梯度校正参数估计（RGC） 5、递推极大似然参数估计算法（RML）(1、Classical particle swarm optimization algorithm matlab code 2、Various types of system identification algorithm)

K-means聚类算法，参加模式识别第二版 清华大学出版社(K-means clustering algorithm, to participate in pattern recognition, the second edition of Tsinghua University Press)