meanshift

产生必需的高斯导函数和目标辐射掩蔽，确定高斯导函数的系数并画出最终信号的psd(have the necessary Gaussian function and goals guided radiation shelter. I. Gaussian function to determine the coefficients and the mapping out of the final signal psd)

这个文件是对Tutoriade的matlab仿真(This is a simulation using matlab Tutoriade)

pso optimization function

大平壁非稳态导热计算，大平壁的初始温度为t0 ，两侧面向温度为tf的介质散热，表面传热系数为h。计算平壁中的温度场。(Ohira wall unsteady heat conduction calculation.Ohira initial temperature of the wall as t0, tf both sides oriented temperatures for dielectric cooling , the surface heat transfer coefficient h . Calculate the temperature field in the flat walls . )

拟使用基于LMS与RLS的自适应算法在MATLAB平台上对带有两个权的自适应线性组合器进行仿真,进而对两类算法的性能作比较，同时也考察了两种算法在不同参数条件下曲线收敛性的变化(Intending to use the LMS and RLS-based adaptive algorithm in the MATLAB platform with two pairs of the right to self-adaptive linear combiner is simulated, and thus the performance of two types of algorithms to compare, but also examines two kinds of algorithms with different parameters under the conditions of convergence of the change curve)

采用遗传算法解决多TSP旅行商问题的matlab工具箱，很有帮助(Using genetic algorithms to solve the traveling salesman problem TSP much matlab toolbox, helpful)

RS-code performance vs BCH-code performance in a communication system.

Applied Numerical Analysis using matlab

用matlab的各种编程例子，可以供大家更好的理解掌握MATLAB，自己觉得挺有用的。(Various matlab programming examples for all to better understand and grasp the MATLAB feel quite useless.)

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.)