mymorletcwt

this is EZW algorithm for matlab

dc motor speed control using fuzzy logic

采用matlab simulink编写的消峰参考设计，可以直接生成verilog代码。消峰主要用于降低无线信号的峰均比，提高功放效率。(Clipping prepared using matlab simulink reference design, you can generate verilog code directly. Consumers peak mainly used to reduce radio signal PAR, improve power amplifier efficiency.)

lab story in matlab at istanbul techn. uni

利用MATLAB程序中的inline和nlinfit两个命令对血药浓度-时间数据进行非线性拟合,求解血管外给药的药动学参数。同时与3P97软件计算结果进行了比较,结果表明,基于MATLAB程序可方便快速地求解血管外给药的各个药动学参数,计算结果与3P97软件计算值相当,且本方法操作简便、界面直观、易于推广,可用于临床教学或实验中药动学参数的计算。 更多还原 (Using MATLAB program inline and nlinfit two commands to the plasma concentration- time data by nonlinear fitting, solving the pharmacokinetic parameters of extravascular administration. Simultaneously with the 3P97 software to calculate the results were compared, the results show that can quickly and easily solve extravascular administration of various pharmacokinetic parameters based on MATLAB, the results calculated with 3P97 software fairly, and that the method is simple, intuitive interface, easy to spread, can be used to calculate the clinical teaching or experimental medicine pharmacokinetic parameters. More Restore)

matlab编写的有关神经网络的三个小程序，其中包含bp网络，lvq网络和sofm网络(matlab prepared by the neural network of three small program, which includes bp network, lvq networks and sofm Network)

matlab tutorial for basic

三阶的MASH 数字 delta sigma modulator 的matlab simulink的文件(3rd order MASH DDSM matlab script)

说明：  深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)

background subtraction