Admittance circle method

prims算法遗传算法 PID 控制，多变量解耦 PID 控制，几种先进的PID 控制，灰色 PID 控制，伺服系统 PID 控制，PID 实时控制，每种方法都通过 MATLAB 仿真程序进行了说明。本书各部分内容既相互联系又相互独立，读者可根据自己需要选择学习。本书适用于从事生产过程自动化、计算机应用、机械电子和电气自动化领域工作的工程技术人员阅读，也可作为大专院校工业自动化、自动控制、机械电子、自动化仪表、计算机应用等专业的教学参考(prims genetic algorithms described in MATLAB tutorial)

快速doa估计方法，包含线阵和平面阵，但精度不是很高(Quick doa estimation methods, including linear array and planar array, but not very high accuracy)

演示红色小球沿一条封闭螺线运动的实时动画。(Red ball shows a closed spiral motion along the real-time animation.)

matlab中的libsvm工具包，是smo支持向量机算法，可用于解决非线性的回归问题。区别于最小二乘支持向量机。(Matlab libsvm Kit is to smo support vector machine algorithm that can be used to solve nonlinear regression problems. Different from the least squares support vector machine.)

Matlab 编写的一个有关支持向量机的程序，有GUII可以设定参数，和显示最终的结果。，已通过测试。 (Matlab prepared a procedure for support vector machines, there are GUI can set parameters, and display the final result. Complete source code, has been tested. )

神经网络30个案例分析源码，又名智能控制30个案例分析,案例二，遗传算法改进的BP神经网络拟合(30 case studies of neural network source code, also known as intelligent control 30 case studies, case two, GA improved BP neural network fit)

极限学习机matlab以及Python程序，最近几年出现的较为新的人工神经网络算法，有基于人脸识别的例子，由作者原网站下载，可运行。(Extreme Learning Machine matlab and Python program, more new artificial neural network algorithm emerged in recent years, there are examples of face recognition based on the original site of the download, you can run.)

利用MATLAB实现的SPIHT算法源代码 MATLAB实现的SPIHT算法源代码(SPIHT algorithm implemented using MATLAB source code MATLAB implementation of the SPIHT algorithm source code)

求一元二次方程ax2 +bx+c=0的根(Find a quadratic equation ax2+ bx+ c = 0 root)

BP（Back Propagation）网络是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。 BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。 它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hide layer)和输出层(output layer)。 通过对信息的提取以及记忆，用作分类、聚类、预测等。 (BP (Back Propagation) network is a pre-press error back propagation algorithm Multilayer feedforward network, is one of the most widely used neural network model. BP network can learn and store a large amount of input- output mode mapping without prior reveal mathematical equations that describe the mapping relationship. It s learning rule is to use the steepest descent method, by reverse spread to constantly adjust the network weights and thresholds and the minimum sum of squared error of the network. BP neural network topology comprises an input layer (input), hidden layer (hide layer) and an output layer (output layer). By extracting information and memory, it is used as classification, clustering, prediction.)