shangzhifa

多普勃效应因子与表观时间和表观长度,网上是要花钱买的，(Multi-factor and the apparent effect of普勃time and the apparent length, the Internet is to spend money, and)

its about generation of ofdm and reception. and also it calculates the ber.

AMRA 模型 修改的Yule_walker方法( modified Yule_walker method for AMRA )

实现了AIC（主动干扰消除）算法，并仿真了AIC算法的性能(Realized AIC (active interference cancellation) algorithm and simulated performance of the algorithm AIC)

本书描述光纤中超连续谱产生,其中包含Matlab源代码，欢迎下载使用(Supercontinuum geration in optical fibers)

increment reference count to io_context.

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.)

径向基函数网络应用实例 希望对大家有帮助(Application of radial basis function networks hope to help)

氧乐果控制过程，利用matlab编程，使用模糊迭代学习控制方法，很好的实现了对氧乐果合成温度的控制。(Omethoate control process, using MATLAB programming, the use of fuzzy iterative learning control methods, to achieve a good control of the Omethoate synthesis temperature.)

svpwm模块，可根据需要形成PWM波形(svpwm block)