利用matlab语言和软件采用粒子群优化算法解决电力系统无功优化问题

matlab 实验，这是上课的实验，有基本应用，适合信号。通信专业初学者入门使用( MATLAB exercises,are suit for students whose majors are signal processing ang communication)

3 phase Active Power filter that is simulated in matlab simulink based on hysteresis current control method

factral comperassion with different AI technique for image and value parameter analysis..................................................

从实用的角度出发，详细介绍了在MATLAB软件环境下，如何利用研 华的数据采集与控制多功能板卡实现数据采集与控制仿真。 本手册适合工程技术人员、科学研究人员和大中专学校的学生参 考。(From the practical point of view, described in detail in the MATLAB software environment, how to use Advantech' s Data Acquisition and Control of multi-board data acquisition and control simulation. The manual for engineering and technical personnel, scientific research and large secondary school students.)

particle filter based on color to track object movement

说明：  MATLAB 代码 第1章 BP神经网络的数据分类——语音特征信号分类 第2章 BP神经网络的非线性系统建模——非线性函数拟合 第3章 遗传算法优化BP神经网络——非线性函数拟合 第4章 神经网络遗传算法函数极值寻优——非线性函数极值寻优 第5章 基于BP_Adaboost的强分类器设计——公司财务预警建模 第6章 PID神经元网络解耦控制算法——多变量系统控制 第7章 RBF网络的回归--非线性函数回归的实现 ....等58章(MATLAB code The first chapter is BP neural network data classification -- speech characteristic signal classification The second chapter is the nonlinear system modeling of BP neural network nonlinear function fitting The third chapter, genetic algorithm optimization BP neural network - nonlinear function fitting The fourth chapter, neural network, genetic algorithm, function extreme value optimization nonlinear function extremum seeking The fifth chapter is based on BP_Adaboost's strong classifier design -- the company financial early-warning model The sixth chapter is PID neural network decoupling control algorithm multivariable system control The seventh chapter is the regression of RBF network the realization of nonlinear function regression .........the last is 58 chapters)

perturb and observe tracking algorithm

有限单元法基础(第2版) 配套Matlab源代码(The basis of the finite element method (2))

matlab所有函数代码一览表，按照字母排序(All functions matlab code list, in alphabetical order)

特征降维方法，其中包含有典型的特征组合方式——PCA和特征选择中的SFFS，SBS，SFS。共四类常用的特征降维方式。(different features dimensionality reduction methods.)