一种视觉识别方法

说明：  电流滞环比较法源程序，绝对好用，自己运行过得，用的是7.0(Hysteresis current source of comparative law, the absolute ease of use, running their own lives, using a 7.0)

求解欧拉方程，简单的Jameson格式。注释少，但是能看懂(Solving Euler equation)

说明：  网络调试助手 打开可用 详细：见文档内readme(Network debugging assistant open available Details: see readme in the document)

LMI的Matlab求解，以及线性非线性控制、基于倒立摆的实例(Matlab solution of LMI, and linear nonlinear control, based on inverted pendulum.)

函数逼近与拟合 240 　　7.1 正交多项式 240 　　7.1.1 正交函数族 240 　　7.1.2 几个常用的正交多项式 242 　　7.2 最佳一致逼近 246 　　7.3 最佳平方逼近 249 　　7.4 最小二乘拟合 252 　　7.4.1 线性最小二乘拟合 253 　　7.4.2 非线性最小二乘拟合 255 　　7.4.3 多元最小二乘拟合 256 　　7.5 有理函数逼近 256 　　7.5.1 连分式逼近 257 　　7.5.2 Padé逼近 259 　　7.6 傅里叶逼近 262 　　7.7 MATLAB自带函数应用 264 　　7.7.1 polyfit函数 264 　　7.7.2 lsqcurvefit函数 266 　　7.7.3 nlinfit函数 267 　　7.7.4 lsqlin函数 268 　　7.7.5 lsqnonlin函数 269 　　7.8 应用案例 270(Matlab function approximation and fitting source code)

文曲星猜数字，给你六次机会猜数字，成功给出提示，是否继续猜数字(Wenquxing number guessing, give you six opportunities numberguess success prompting whether to continue numberguess)

说明：  LL（1）文法的识别，和LL（1）分析表的构造(Recognition of LL(1) Grammar and Construction of LL(1) Analysis Table)

SVDD参数C和g的网格优化，根据faturo大神的SVM参数优化改编(Grid optimization of SVDD parameters C and G)

说明：  利用matlab的simulink完成了倒立摆的LQR仿真，能实现位置跟踪，效果很不错(Matlab simulink completed the inverted pendulum LQR simulation, can achieve the position tracking, the effect is very good.)

说明：  为了提升配网供电可靠性的预测精度!提出了基于主成分分析和粒子群优化极限学习机的配网供电可靠 性预测模型$ 从多方面分析影响供电可靠性的指标!利用主成分分析得到综合变量!实现对数据的降维$ 在此基 础上!构建人工神经网络并利用粒子群算法优化极限学习机的输入权值和阈值!完成对训练供电可靠性预测模型 的训练$ 以某大型电网的 ?L 个供电局样本 !% 种影响供电可靠性因素为例进行仿真分析!并将 E S R C E FQ C 4 G D算 法与 ! 种回归拟合算法对比!验证了该方法的有效性(It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can p)