利用matlab实现H-infinity鲁棒控制

all possible permutations of the numbers 1:n(all possible permutations of the numbers 1 : n)

说明：  MIMO信道初始化的MATLAB仿真源程序(intimate mimo channel source)

MATLAB2010的教程，电子书籍，很详细(MATLAB2010 tutorials, e-books, very detailed)

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模糊逻辑的几个应用举例。 《matlab辅助模糊系统设计》(Several application examples of fuzzy logic. " Matlab auxiliary fuzzy system design" )

通过Matlab 平台实现了排队论中M/M/1/N/∞ 模型的过程仿真。(Matlab platform for process simulation of queuing theory M/M/1/N/∞ model.)

这是Simulink关于模拟调幅的模型。希望对初学者有用。(This film is based on Simulink simulation model of amplitude modulation. Hope useful for beginners. )

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.)

用matlab程序实现对音频信号幅度调制的仿真结果(Matlab program used for audio signal amplitude modulation simulation results)

模拟退火算法模拟退火算法模拟模拟退火算法退火算法(Simulated annealing algorithm simulated annealing algorithm simulated annealing algorithm simulated annealing algorithm)