cordic_1-0

应用多元高斯声束叠加方法建立粉末合金构件的声场模型(sound field simulation sound field simulation sound field simulation )

遗传算法（Genetic Algorithm）是一类借鉴生物界的进化规律（适者生存，优胜劣汰遗传机制）演化而来的随机化搜索方法。

求解Lambert问题。 输入：轨道上两个点的位矢R1、R2，以及从R1到R2所用的时间t(缺点是脉冲太大)。 输出：质点在R1端点的速度V1，质点在R2端点的速度V2. 此函数用于：在给定轨道上两个点的位矢R1、R2，以及从R1到R2所用的时间t。求解V1、V2。再调用前面的函数就可以确定轨道根数，及任意时刻质点的状态。(To solve the Lambert problem. Input: track two-point position vector R1, R2, and R1 to R2 used in the time t (The disadvantage is that the pulse is too large). Output: particle R1 endpoint speed V1, the particle in the R2 endpoint speed V2. This function is used: two points in a given orbit position vector R1, R2, and from R1 to R2 with the time t. Solve the V1 and V2. And then call the previous function can determine the orbital elements, and any moment of the particle' s state.)

this is a simple and comfort system for recording voice

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.)

核主成分分析方法，是主成分分析的一种改进算法，是一种非线性的特征提取方法。 (Kernel principal component analysis, is the principal component analysis of an improved algorithm, is a nonlinear feature extraction method.)

说明：  c常用函数，适合初学者使用，里面有详细的注释(c common function, for beginners, there are detailed notes)

[matlab source code]CPUmeultithread

matlab中VCA算法的事例 用于高光谱像元解混(the algorithm of VCA)

逆合成孔径雷达单点目标仿真程序 但点目标 新手啊 有什么不对的 请指教( for matlab ISAR)