资源列表
zuixiaoercheng
- 实现基于加权最小二乘法的电力系统状态估计算法，以于老师写的书里面四节点算例仿真-Implementation based on weighted least-square method of power system state estimation algorithm, with the teacher wrote the book four node example simulation
test_code-bfq2pp
- 用MATLAB软件仿真常用于毕设中的sdf加密算法，算法仿真了sdf条件的误码率的最优解，成功实现了整个ssf加密过程。-Simulation using MATLAB software commonly used in the complete set of sdf encryption algorithm, the optimal solution algorithm simulation sdf error rate conditions, the successful implement
MT1D
- 大地电磁一维正演程序代码，能够很详细的说明二维模型的正演算法，简单易懂，适合初学者入门。-agnetotelluric one-dimensional forward program code, can be very detailed instructions are algorithm of two-dimensional model, simple, suitable for beginners entry.
Spread-of-wave-equation
- 一种地震或地质雷达方向上波动方程在介质中传播发生反射与透射模拟的程序，简单易懂，适用于初学者。-Direction of an earthquake or geological radar wave equation in the medium dissemination on the reflection and transmission simulation program, simple, suitable for beginners.
Noise-adaptive-fuzzy-switching-median-filter-for-
- Noise adaptive fuzzy switching median filter for salt and pepper noise reduction in matlab
bh_tsne.tar
- 本代码实现TSNE降维，首先利用PCA进行主成分分析，选取何时的特征再降维-dimension reduction for TSNE，we first use Principal Component Analysis to dimension reduction.
discrete-mpc-with-constraints
- 模型预测控制 多输入多输出 2自由度汽车模型 多目标有约束优化问题-model predictive control with 2dof vehicle model with constraints optimization problem
8-dof-vehicle-dynamic
- 8个自由度汽车建模，适合复杂的角速度偏移率控制-8 dof vehicle modelling to control yaw rate
fmincon-in-matlab-and-example
- fmincon函数中文解释及例子,pdf文件，有比较详细的解释-fmincon and its example
fenlei
- 针对广泛用于推荐算法研究的movielens数据集，本程序用于统计用户评分时间，寻找用户评分规律。-Recommendation algorithm is widely used for research movielens data set, the program for statistical Rating time, looking user rating rule.
CMAC
- 基于信任度分配的cmac小脑神经网络算法-CMAC MATLAB
DeepLearnToolbox_CNN_lzbV2.0
- DeepLearnToolbox_CNN_lzbV2.0 深度学习,卷积神经网络,Matlab工具箱 参考文献: [1] Notes on Convolutional Neural Networks. Jake Bouvrie. 2006 [2] Gradient-Based Learning Applied to Document Recognition. Yann LeCun. 1998 [3] https://github.com/rasmusberg