资源列表
BP
- 误差反传网络(BP)特点：1)对原始数据的分布型式无要求；2)已知模型的类型应比较全面；3)适用于多目标模式识别；4)外推能力有限；5)定性数据和定量数据混合处理；6)当加入新模型时需要重新训练网络；7)不能用于数据插值。 -1) the distribution pattern of the original data requirements 2) known model types should be more comprehensive 3) suitable for multi
Hopfield
- 循环反馈网络(Hopfield)特点：1)定性数据的模式识别；2)依靠吸引子来作模式识别；3)其功能可由BP网络来实现，但速度较快。 -Loop feedback network (Hopfield) features: 1) the qualitative data of the pattern recognition 2) rely on attractor for pattern recognition 3) its function can be made of BP netwo
Kohonen
- 自组织网络(Kohonen)特点：1)适用于超大样本的无监督分类；2)其结果常常需要与统计分析一起使用来解释分类结果；3)能够识别新类型，但功能较差。 -1) is suitable for large sample of unsupervised classification 2) the results often need to use with statistical analysis to explain the classification results 3) to ide
RBF
- 径向基函数网络(RBF)特点：1)可用于任意维空间的插值；2)训练速度和插值速度较慢；3)一旦训练成功，只要存储权系数矩阵即可，适用于海量数据的插值；4)当数据不全时，可以用于数据补全。 -Radial basis function (RBF) network features: 1) can be used in any dimensional space interpolation 2) interpolation and the training speed slower 3) o
nine_bus_pf
- 使用simulink仿真，实现潮流计算。。。非常详细，可以对学习潮流计算和simulink的提供帮助。 -Use simulink simulation, realization flow calculation. ʱ ?? ʱ ?? Very detailed, can help to study the trend of computing and simulink.
ORPD
- 使用粒子群算法进行无功调度，考虑经济性，具有比较好的效果。可以学习-Use PSO reactive power dispatch, considering the economy, with relatively good results. You can learn
code_bp
- 人工智能神经网络的matlab实现，有具体代码及多个M文件-Artificial neural network matlab realize, there are specific codes and a plurality of M files
suiji
- 人工智能神经网络中随机神经网络的实现，有M文件，可修改-Artificial neural network stochastic neural network to achieve, there are M-file, you can modify
code_xianxing
- 线性神经网络的实现，包括13个M文件，可以用来学习，非常实用-Linear neural network implementations, including 13 M-file can be used to learn, very practical
code_fankui
- 人工智能反馈神经网络的matlab编程，包含丰富的程序资源-AI feedback neural network matlab programming, the program contains a wealth of resources
activity-recognition-based-on-SVM
- 基于支持向量机的人类活动识别，以日常生活中的10个活动进行识别。-Support Vector Machine (SVM) was first proposed in 1995 by Cortes and Vapnik [15] for solving classification and regression problems. The solving strategy of SVM on the multiple classification problems is com
activity-recognition-based-on-DRNN
- 基于多层神经网络的人类活动识别，智能家居领域的一项重大突破。-Activity recognition has received increasing attention from the machine learning community. Of particular interest is the ability to recognize activities in real time streaming data, but this presents a number of