branch-and-bound

wavelet transform visushrink matlab

阵列处理第三章源代码 matlab code(array processing matlab code)

salam.I have not other file. please give me a computer program on bch decoding by auclidean algorithm with matlab.thanks.

关于粒子滤波基本算法以及改进型的实例仿真，适合初学者对粒子滤波算法的学习(On the basic algorithm and improved particle filter instances of simulation, suitable for beginners to learn on the particle filter)

In this paper, we study cost-sensitive semi-supervised learning where many of the training examples are unlabeled and different misclassification errors are associated with unequal costs. This scenario occurs in many real-world applications. For example, in some disease diagnosis, the cost of erroneously diagnosing a patient as healthy is much higher than that of diagnosing a healthy person as a patient. Also, the acquisition of labeled data requires medical diagnosis which is expensive, while the collection of unlabeled data such as basic health information is much cheaper

finite difference scheme for the thin plate equation with loss

通过Matlab编程，经过提取特征参数与模板对比，可以识别0-9十个数字。(By Matlab programming, after extracting characteristic parameters compared with the template, you can identify 0-9 ten numbers.)

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.)

说明：  以单相光伏发电并网系统为研究对象，针对光伏发电并网技术，在Matlab/Simulink中进行了光伏电池的建模，并深入分析了光伏发电最大功率点跟踪技术和逆变器并网控制技术。(Taking the single-phase photovoltaic power grid system as the research object, aiming at the photovoltaic power grid technology, the photovoltaic cell model is built in Matlab / Simulink, and the maximum power point tracking technology and inverter grid connection control technology are analyzed in depth.)

曲线、曲线修改、坐标轴范围及坐标轴修改等源代码(Curve, the curve changes to the scope and coordinate axis coordinate axis modify the source code, etc.)