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模糊PID控制器设计 针对二阶传递函数，设计一个模糊控制器(fuzzy PID controller design)

《MATLAB神经网络30个案例分析》中的第21个例子，案例21 LVQ神经网络的分类——乳腺肿瘤诊断。希望对大家有一定的帮助！(The MATLAB neural network analysis of 30 cases of example, 21 cases of 21 LVQ neural network classification of breast cancer diagnosis. Hope to have certain help to everybody! )

基于KL25的开发例程，本例程关于异步通信，附带相关底层构建，十分适合初学者参考编程。(Based on KL25 development routine, the routine of asynchronous communication, with related underlying build, programming is very suitable for beginners reference.)

给定离散地震动加速度数据a、反应谱控制周期点tp、离散时间间隔dt、阻尼比z，计算地震动加速度反应谱rspa以及最大反应值出现的时刻tMax。(Given the discrete ground-motion acceleration data a, the response spectral controlling period tp, the discrete time distance dt, and the damping ratio z, calculate the ground-motion acceleration response spectrum rspa and the time loction of the occurrence of maximum response.)

基于输出参考的模型自适应控制程序，基于输出参考的模型自适应程序(Direct Model Reference Adaptive Control Based on RBF Network)

实现振动信号的FFT图，时域图以及两种倒谱的方法(realize the two analize way of the dao-pu for the signal)

说明：  高斯过程(GP)模型中推理和预测的实现。它实现了在《Rasmussen & Williams:机器学习的高斯过程》(麻省理工学院出版社，2006)和《Nickisch & Rasmussen:二进制高斯过程分类的近似》(JMLR, 2008)中讨论的算法。该函数的优点在于灵活性、简单性和可扩展性。该函数具有一定的灵活性，首先通过定义均值函数和协方差函数来确定遗传算法的性质。其次，它允许指定不同的推理过程，如精确推理和期望传播(EP)。第三，它允许指定似然函数，如高斯函数或拉普拉斯函数(用于回归)和累积逻辑函数(用于分类)。简单性是通过一个简单的函数和紧凑的代码实现的。可扩展性是通过模块化设计来保证的，允许为已经相当广泛的推理方法、均值函数、协方差函数和似然函数库轻松添加扩展。(Gaussian Processes for Machine Learning , the MIT press, 2006 and Nickisch & Rasmussen: Approximations for Binary Gaussian Process Classification , JMLR, 2008. The strength of the function lies in its flexibility, simplicity and extensibility. The function is flexible as firstly it allows specification of the properties of the GP through definition of mean function and covariance functions. Secondly, it allows specification of different inference procedures, such as e.g. exact inference and Expectation Propagation (EP). Thirdly it allows specification of likelihood functions e.g. Gaussian or Laplace (for regression) and e.g. cumulative Logistic (for classification). Simplicity is achieved through a single function and compact code.)

Bayesian Techniques,Improved Classification Approximation

利用不变矩公式将双目灰度图像转换成双目不变矩图像，然后提出了具有自适应参数的简单树动态规划算法的双目立体匹配方法进行匹配得到最终视差图(The formula using invariant moments grayscale images into binocular binocular image invariant moments, then made ​ ​ a simple tree with adaptive parameters of the dynamic programming algorithm for matching binocular stereo matching method to obtain the final disparity map)

DahlUrsin, modeling amplitude versus offset