MATLAB

用同态滤波的方法增强图像对比度，解决因为光照问题引起的图像模糊问题。(By homomorphic filtering method to enhance image contrast, to solve the problem because the light-induced image blurring problem.)

这是关于基本GA算法和改进GA算法的程序，包括循序选择GA，大变异GA，自适应GA等。(This is about the basic GA algorithm and the improved GA algorithm procedures, including sequential selection GA, a large variation of GA, such as adaptive GA.)

说明：  不动点迭代方法也是一常用的计算非线性方程组的解法。(Fixed point iteration method is also a common calculation method of nonlinear equations.)

说明：  photovoltaic cell model

abe model matlab very good

用于决策树中对数据进行分类，适合机器学习等领域(For a decision tree to classify data for machine learning and other fields)

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.)

压缩感知技术，信号重建matlab程序代码。matlab2010b(compress sensing)

离散优化，主要用于离散数据的优化处理，适用于初学者的简单程序( Discrete optimization, mainly used for the optimization of discrete data, applies a simple program for beginners)

说明：  克里金代理模型的DACE工具箱，可用于优化计算(The dace toolbox of Kriging agent model can be used for optimization calculation)