cascadethreephase

windows下matlab编程 虚拟现实工具箱(matlab Virtual Reality Toolbox 4.8)

这个是基于PSO粒子群算法的RBF径向基网络(This is based on particle swarm optimization PSO RBF radial basis function network)

CC2530NDK实验1——Z-STACK SensorDemo实验(CC2530NDK Experiment 1- Z-STACK SensorDemo experiment)

遗传算法工具箱 给出遗传算法中所有能用到的所有函数，给使用者带来方便。(Genetic Algorithm Toolbox Genetic Algorithm give all to all the functions can be used to bring convenience to users.)

运行程序得到微分方程中x1随参数omega变化的分岔图。omega的范围选取可以调节。(Run the program to be differential equations with parameters x1 bifurcation diagram omega changes. omega scope of selection can be adjusted.)

EMD的在MATLAB的环境下进行的分解和仿真效果图(The EMD decomposition MATLAB environment and simulation results Figure)

THIS CODE IS REG.IMAGE STEGNOGRAPHY

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.)

Select FIS Generation Method. , , Grid Partitioning

以Matlab为应用，比较详细地介绍了如何画波特图的一些例子。(Examples on Bode Plot.)