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机动目标跟踪的交互式多模型自适应滤波算法的程序设计代码 单传感器方位角和高低角(Maneuvering target tracking interactive multi-model adaptive filtering algorithm programming code)

电骡0.45b源码.rar (-- Mule 0.45b source. Rar-- Mule 0.45b source. Rar)

说明：  产生TU信道，可自行设置参数，支持MIMO功能(create TU channel，can set your own parameter,support MIMO)

本程序描述了kalman滤波算法，利用初学者加深对kalman滤波算法的理解。(This procedure describes the kalman filter algorithm, the use of beginners to deepen the understanding of the kalman filter algorithm.)

荧光光谱聚类分析软件，可对物质的荧光光谱进行分类。(Fluorescence spectroscopy analysis software for clustering)

本程序是一个数据预处理的程序，首先通过KL变换将数据进行一次降维，然后通过LDA对数据进一步降维(This program is a data preprocessing procedure, first by a KL transform data dimensionality reduction, then the data further by LDA dimensionality reduction)

Read_Me: Instructions for setting up matlab speech processing exercises.

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实现RLS算法 画出原始信号和处理后的信号进行对比 (RLS algorithm using Matlab to draw to achieve the original signal and processed signal to compare)

PURPOSE: E_GARCH(P,Q) parameter estimation with different error distributions, the Normal, The T, and the Generalized Error Distribution