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阻容降压原理 阻容降压原理 阻容降压原理(Principles of RC Buck)

MIT--航空航天工程中的计算方法_概率论方法实例——涡轮叶片中的热传导问题 MIT公开课程--航空航天工程中的计算方法 这门课程适用于在航空和航天工程中出现的计算方法的入门学习。其应用是从航空宇宙结构，空气动力学，动力学和控制，和航空宇宙系统选出的。其技术包括：常微分方程系统的数值积分；偏微分方程式的有限差分,有限体积, 和有限元的离散化；数值线性代数；本征值问题和带有约束的最优化。(MIT- Aerospace engineering calculation methods of probability theory _ instance- turbine blade heat conduction problem in the MIT Open Course- Aerospace engineering calculation methods for this course in aviation and aerospace engineering calculation methods appear of getting started. The application is from aerospace structures, aerodynamics, dynamics and control, and aerospace systems selected. The technologies include: numerical integration of ordinary differential equations systems partial differential equations of finite difference, finite volume, and finite element discretization numerical linear algebra eigenvalue problems with constraints and optimization.)

ARIMA交通流预测程序，利用perms上的数据进行预测(ARIMA traffic flow prediction program)

建立了基于matlab语言的四位全加器仿真模型，通过了系统验证。(Matlab language is established based on four full adder simulation model, verified by the system.)

Matlab demo code for the cyclic order preserving assignment problem with application to shape matching(Matlab demo code for the cyclic order prese rving assignment problem with application to s CPR matching)

粒子群算法，自己编写的，可以用，matlab语言环境(Particle swarm optimization, I have written, you can use, matlab language environment)

matlab 信号处理工具箱，信号系统模型，线性非线性模型(matlab signal-processing-toolbox)

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.)

案例设计源程序，matlab动态创建控件，有函数有界面的设计(Case design source, matlab dynamically created controls, there are functions with interface design)

计算洛伦兹系统的lyapunov指数，matlab语言编写的(Computing the Lyapunov Exponents of the Lorenz System)