A258

比较四分量信号的不同时频分布结果,小波分析。(Compare the results of time-frequency distribution of the four components of the signal)

说明：  综合实验编写的航迹发生器及SINS系统仿真 仔细看明白 绝对可用 simlink 编写 实时性强(The preparation of a comprehensive test of track generator and SINS system simulation closer look to understand the absolute time available to prepare strong simlink)

programme de simulation MAS onduleur

光伏并网仿真模块，采用PSO（扰动观察）完成MPPT(PV model with MPPT)

泰勒阵列综合算法，能够计算出点源激励以及最终的方向图等等(Taylor array synthesis algorithm)

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.)

很好的matlab基础入门教学ppt(中科院)(a very good book for matlab study)

说明：  软测量氧含量原始数据及matlab工况聚类代码(Measuring the oxygen content of soft raw data and clustering matlab code condition)

大规模多输入多输出非平稳的无线信道冲激响应(Large-scale multi-input multi-output non-stationary wireless channel impulse response)

说明：  麻雀搜索算法(Sparrow Search Algorithm, SSA)是于2020年提出的。SSA 主要是受麻雀的觅食行为和反捕食行为的启发而提出的。该算法比较新颖，具有寻优能力强，收敛速度快的优点(Sparrow search algorithm (SSA) was proposed in 2020. SSA is mainly inspired by the foraging behavior and anti predation behavior of sparrow. The algorithm is novel and has the advantages of strong optimization ability and fast convergence speed)