lpcconv

OFDM详细的MATAB仿真，包括了每一个详细的步诌(Detailed OFDM simulation MATAB)

神经网络使用例子，主要用于图像数据处理以及数据挖掘(Neural Network examples)

output switching between 2 input switch

计算三维数据的法矢量并进行调整，里面包括了自己做的实验数据和结果-(The method to calculate the three-dimensional vector data and make adjustments, which includes the experimental data and the results of their own-)

控制问题转化为了数学规划问题。推导出目标函数对于待求参数的梯度公式，并利用序列二次规划算法求出最优控制量(the problem is changed into mathematical programming problem and formulae are derived for computing the gradients, also, a computational method for finding the optimal control is obtained by using the Sequential Quadratic Programming (SQP) algorithm.)

方波信号的混沌阵子仿真模型，可用来仿真信号的最低信噪比！(The stream of square wave simulation model of the chaotic signal can be used to simulate the signal of the minimum signal to noise ratio!)

Fisher线性判定函数，输入训练集及测试集，输出错误率和线性判别函数。(The Fisher Linear determine the function, enter the training set and test set, the output error rate and linear discriminant function.)

MATLAB下的语音识别程序，已经做成了gui界面(MATLAB under the speech recognition program, has made a gui interface)

计算平行线耦合的奇偶模阻抗值和切比雪夫低通原型滤波器的耦合系数(caculate the parrellel coupleline odd and even resistance value and based on the Chebyshev lowpassfilter coupled cofficients)

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.)