资源列表
demo3
- 在demo中，用EKF和有噪声的EKF训练非线性、非平稳数据。-In this demo, I use the EKF and EKF with noise adaptation to train a neural network with data generated a nonlinear, non-stationary state space model. Adaptation is done by matching the innovations ensemble covariance
Bland-Altman
- Bland-Altman算法实现，包含比值法和差值法，包含做图功能-Bland-Altman algorithm to achieve, including the ratio method and the difference method, including the map function
open-mirror
- 电力负荷预测在国民的生产生活方面发挥着重要职能。精确的电力负荷预测，对电力系统的生产安排、安全分析和经济调度发挥着极其重要的作用，而它的预测精度则影响着电力市场的社会和经济效益。-: U7535 u529B u753 u5B09 u5129 u5B09 Jiaotong University Wang Zhao
kNN
- 机器学习实战中，K近邻算法的实现。包括算法实现，算法分类测试-Machine learning combat, the realization of K nearest neighbor algorithm. Including the algorithm, the algorithm classification test
bayes
- 机器学习实战中，实现贝叶斯分类算法。包括算法的实现，必要的注释，分类测试-Machine learning actual combat, achieve Bayesian classification algorithm. Including the implementation of the algorithm, as required notices, classification test
svmMLiA
- 机器学习实战中，SVM向量机算法的实现。包括必要的注解、分类效果的测试-Machine learning actual combat, achieve SVM vector machine algorithm. Including tests necessary notes, classification effect
reconstitution
- reconstitution函数，该函数用来重构相空间。-The reconstitution function, which is used to reconstruct the phase space.
MGMM_Particle-Filter
- 本文分别实现了整体模板更新和选择性子模块更新方法，以适应运动目标的运动姿态变化以及运动背景变化，并将其分别与粒子滤波目标跟踪算法相结合，以提高跟踪的鲁棒性。-This thesis studies and implements a total target model updating method and a selected sub-model updating method, and then combines it with the particle filter algorithm f
Machine-Learning-in-Action
- 分析常见的机器学习算法，并进行实际的应用，能够很好的熟练掌握-Analysis of common machine learning algorithms, and the actual application, can be a good grasp
ELMregression
- 极限学习机在回归拟合问题中的应用，拟合度良好，代码经过验证绝对没有错误哦-Extreme learning machine in the application of regression fitting, the fitness is good
ESN
- 人工神经网络中——回声状态网络的一些应用说明，在MATLAB中可以实现，性能良好。-Artificial neural network, echo state network of some applications that can be implemented in the MATLAB, and the performance good.
elm_kernel
- 极限学习机的算法改良，在极限学习机的基础上加上核函数，使其性能更好-Limit of machine learning algorithm is improved, on the basis of the extreme learning machine and kernel function, make its performance better