资源列表
DCRCB
- 双约束波束形成算法，相比传统的鲁棒波束形成算法，加入了波矢量的范数约束，-Dual constraint beamforming algorithm
The-report-of-neural-networks
- 随着神经网络研究的深入，神经网络在理论上有了很大突破，并在实践中发挥着越来越重要的作用。本文介绍了径向基网络，支撑矢量机，小波神经网络，反馈神经网络这几种典型的神经网络结构模型、特点及应用。- With in-depth study of neural networks, neural networks have great breakthrough in theory and in practice is playing an increasingly important role. This
the-neural-network--general
- 文章介绍了神经网络和人工智能的历史、现状和发展方向，以此为基础，比较神经网络和人工智能，指出了它们的区别和联系，并对神经网络和人工智能在未来的前景进行了一定评价。 -This paper introduces the Artificial Neural Network (ANN) and Artificial Intelligence’s (AI) history, actuality and their develop directions. On the basis of that,
Pattern-Recognition
- 模式识别与机器学习，主要从算法与原理的角度来讲述-Pattern recognition and machine learning, mainly from the perspective of algorithm and theory about
Project
- Project Group for Robot Boxes search and obstacle Avoidance
juleisuanfa
- vc实现的聚类算法，可以输入手写数字，程序自动对输入的手写数字进行聚类-vc clustering algorithm to achieve, you can enter handwritten digits, the program automatically cluster the input of handwritten numbers
artifical-fishPnfqga
- 人工鱼群算法与量子遗传算法的结合，还运用到了旋转门-AFSA combination of genetic algorithms and quantum, but also applied to the revolving door
80citesyiquduanfa-
- 蚁群算法的程序和引言，含有坐标城市的目的信息。-Ant colony algorithm of procedures and the introduction, the purpose of containing the coordinates of the city' s information.
Result2eyiqunasaune0
- 蚁群算法的程序和引言，含有坐标城市的目的信息。-Ant colony algorithm of procedures and the introduction, the purpose of containing the coordinates of the city' s information.
yiqunadsuanf
- 蚁群算法的程序和引言，含有坐标城市的目的信息。-Ant colony algorithm of procedures and the introduction, the purpose of containing the coordinates of the city' s information.
yiqusaunfachengcude-
- 蚁群算法的程序和引言，含有坐标城市的目的信息。-Ant colony algorithm of procedures and the introduction, the purpose of containing the coordinates of the city' s information.
PCA
- 该程序主要是用于模式识别对样本进行降维处理，该程序是通过PCA的方法实现降维-The program is mainly used for pattern recognition to reduce the dimension of the samples, the program is achieved through the method of PCA dimensionality reduction