资源列表
Libsvm
- 人工智能领域中机器学习所用的支持向量机算法，用C++ & Python编写，可以直接使用。-Artificial Intelligence field: Machine Learning Supported Vector Machine C++ & Python Source Code you can use it as you like
OnRelationalPossibilisticClustering
- 一篇基于可能性理论的数据聚类分析的经典文献。很值得阅读，有一些未解决的难点，可以进一步深入研究！-The possibility of a theory based on cluster analysis of data from the classical literature. Is worth reading, there are some unresolved difficulties, can be further in-depth study!
beijing-Bus--change-system
- 用c++写的北京市公交查询系统，对想要设计查询系统的大作业很有帮助-The Beijing Bus with c++ write query system
Immunity-clone-algorithm-with-mutation-coevolution
- 利用免疫系统的克隆选择机制,提出一种用于函数优化的算法. 算法的主要特点是:在迭代过程中,不仅抗体得到进化,同时建立变异向量集,令变异向量同步进化,协同工作,达到优化的目的. 仿真实验表明,所提出的算法能以较快的速度完成给定范围的搜索和全局优化任务-By using the clonal selection echanism of the immune system , a method for function optimizing is proposed. The character of
danshuchu
- 神经网络C语言实现，实现了多个输出的功能，具体可见里面使用说明。-Neural network C language, to achieve a multiple output function, which shows the specific instructions.
MATLAB作业孙振红
- 基于人工神经网络，通过原位测量得到的海底沉积物的声速值来反演海底沉积物的物理力学性质(Based on artificial neural network, the physical and mechanical properties of seafloor sediments are retrieved by the sound velocity of seafloor sediment measured in situ)
object-detection-master
- 基于深度学习的目标检测方法，效果很好，可以进行学习(Object detection method based on deep learning)
Kmeans
- k-MEANS-CLASSFIER. IMPLEMENTED IN MATLAB.
神经网络模式识别及其实现
- 介绍了神经网络模式识别的基本知识，适合于模式识别领域的人员-it introduces the ABC of the pattern recognation of NN, it is for the people of pattern recognation
Two_papers_about_Differential_Evolution
- 两篇比较新的差分进化算法文章，对算法的搜索效率进了改进。-Two later papers about Differential Evolution improve the search fficiency.
nnmc
- 是神经网络的模式识别源代码，可以直接使用；很好用的！-Is a neural network pattern recognition source code, can be used directly good use!
Optimization-design-experiment2
- 从数控机床能耗角度出发，以切削参数为变量，以降低数控机床能耗为目标，在实际加工经验公式的基础上，考虑机床性能和刀具约束条件，建立数控机床能耗模型，采用粒子群优化算法对目标函数寻优求解，利用优化后的切削参数进行加工，能明显地降低能耗。-From the perspective of CNC machine tool consumption to cutting parameters as variables , in order to reduce the energy consumption o