chp2ex1gui

MATLAB编程入门PPT，快速学会MATLAB基础，进行数值计算编程(Introduction to MATLAB programming PPT, quickly learned the basis of MATLAB, the numerical calculation programming)

RSSI Localization algorithm

matlab simulink 模糊控制器模型(matlab simulink fuzzy controller model)

蒸发波导高度的计算及部分因素对于高度的影响图(Evaporation duct height and some of the factors to calculate the height of the influence diagram)

最短路分配方法流程图的matlab编码（全有全无配流法）(The shortest path distribution method)

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.)

用C++实行数值插值运算并用matlab绘图(With C++ Implement numerical interpolation matlab computing and graphics)

Login password filters in WinXP login screen.

BP神经网络算法matlab实现和matlab工具箱使用实例(BP neural network algorithm matlab matlab toolbox to implement and use cases)

给定观测序列 O=O1O2O3…Ot和模型参数λ=(A,B,π)，怎样有效计算某一观测序列的概率，进而可对该HMM做出相关评估。例如，已有一些模型参数各异的HMM，给定观测序列O=O1O2O3…Ot，我们想知道哪个HMM模型最可能生成该观测序列。通常我们利用forward算法分别计算每个HMM产生给定观测序列O的概率，然后从中选出最优的HMM模型。(Given the observation sequence O = O1O2O3 ... Ot and model parameters λ = (A, B, π), how to effectively calculate the probability that a single observation sequence, and thus can make the relevant assessment of the HMM. For example, there are a number of different model parameters HMM, given the observation sequence O = O1O2O3 ... Ot, we want to know which model is most likely to generate the HMM observation sequence. Usually we use forward algorithm calculates the probability of a given observation sequence O generated for each HMM, HMM and then to choose the best model.)