资源列表
tbtained-numerical
- 用数值方法来求解雅克比矩阵的迭代过程,并且有仿真结果(The iterative process of Jacobian matrix is solved by numerical method, and the simulation results are obtained.)
13898405hvdc-simulink
- 主要用于电力系统的改善，电能质量，波形的改善，抑制电力系统的振荡，直流输电(It is mainly used for power system improvement, power quality, waveform improvement, and suppression of power system oscillation.)
convLSTM_minimum-master
- 卷积长短期机器模型用于预测空间上与时间上的值，是基于LSTM模型的改进算法(Convolutional long-term and short-term machine model is an improved algorithm based on LSTM model for predicting spatial and temporal values.)
opnet仿真实例（5个）OPNET BOOK CODE
- opnet仿真的几个例子，里面有详细的功能介绍和流程(OPNET simulation of several examples, which have detailed functional introduction and process.)
RBM-DBN
- 有限玻尔兹曼机、深度置信网的Matlab实现，用mnist数据进行验证，对理解深度学习原理有帮助。(A Finite Boltzmann machine and deep belief network are implemented in MATLAB and verified with MNIST data. It is helpful to understand the principle of deep learning.)
短时功率谱密度
- 语音信号是一个非平稳时变的信号，所以用计算稳态信号的方法计算功率谱密度函数没有太大意义，看不出信号的动态变化，"gonglvpu"给出计算短时功率谱密度函数(Speech signal is a non-stationary time-varying signal, so it is not very meaningful to calculate the power spectral density function by calculating the steady-st
MFA 边缘Fisher分析
- MFA 边缘Fisher分析 降维算法，是基于LDA的改进
KOSELM-standalone
- kerne online sequential Extreme learning machine
Autoencoder_Code
- Hilton的自动编码器实现代码(rbm based on matlab)
深度学习入门：基于Python的理论与实现.pdf+代码
- 本书是深度学习真正意义上的入门书，深入浅出地剖析了深度学习的原理和相关技术。书中使用Python3，尽量不依赖外部库或工具，从基本的数学知识出发，带领读者从零创建一个经典的深度学习网络，使读者在此过程中逐步理解深度学习。书中不仅介绍了深度学习和神经网络的概念、特征等基础知识，对误差反向传播法、卷积神经网络等也有深入讲解，此外还介绍了深度学习相关的实用技巧，自动驾驶、图像生成、强化学习等方面的应用，以及为什么加深层可以提高识别精度等“为什么”的问题。(This book is a true sen
confusion_matrix1
- 只有一个文件，调用函数即可生成混淆矩阵，参数可在文件中更改(Call the function to generate a confusion matrix, parameters can be changed in the file)
GAN-master
- 关于各种GAN的代码实列，包括ｃｇａｎ，ｉｎｆｏｇａｎ，ｗｇａｎ等。(Code listings for various GANs, including cgan, infogan, wgan, etc.)