资源列表
QACO
- 量子蚁群优化算法，用于求解复杂优化问题~(Quantum ant colony optimization algorithm for solving complex optimization problems~)
适用量子粒子群算法
- 适用量子粒子群算法获得所需的最优值，可以依概率收敛到全局最优，能有效求解复杂优化问题(Quantum Particle Swarm Optimization (QPSO) can be applied to obtain the desired optimal value, which can converge to the global optimum according to probability, and can effectively solve complex optimizatio
量子行为的粒子群算法-SVM
- 改进量子粒子群算法，用于优化支持向量机参数，用IRIS数据验证(An improved quantum particle swarm optimization (QPSO) algorithm is used to optimize the parameters of support vector machine (SVM), which is validated by IRIS data.)
动态蜂群算法源程序
- 动态蜂群算法源程序，实现复杂优化的求解~~~(Dynamic swarm algorithm source program to achieve complex optimization solution ~~~)
模拟退火算法
- MATLAB模拟退火算法，包含两个程序，一个是优化一元函数的，一个是优化多元函数的(MATLAB simulated annealing algorithm, including two programs, one is to optimize the unary function, one is to optimize the multivariate function)
Lipschitz指数和模极大值
- 本程序主要用于求解信号小波变换后，信号奇异点处的李氏指数和模极大值，对信号的断点进行定量的描述，可用于信号故障诊断。(This program is mainly used to solve the Lie index and the modulus maxima at the signal singular point after signal wavelet transform, and quantitatively describe the breakpoint of the signal
FDDL
- 基于Fisher字典学习的稀疏表示分类算法。(Sparse representation classification algorithm based on Fisher dictionary learning.)
models.geomech.triaxial_test
- 岩石试件三轴压缩试验的数值模拟模型，可以得到应力应变曲线图。文件为建模步骤，模型较大，无法上传，需要者可联系。(The stress-strain curve can be obtained by the numerical simulation model of triaxial compression test of rock specimens. The file is a modeling step. The model is large and can not be uploaded.
VRPTW-ga
- 带时间窗的车辆路径问题求解的python代码(VRP with time window(python))
硬海底
- FCT消除频散(FCT elimination dispersion)
深度学习中文版花书
- 「Deep Learning」从深度学习的基础知识和原理一直讲到最新的方法，而且在技术的应用方面也有许多具体介绍，不仅关注实用的技术教程，还体现了较强的学术性，涵盖AI领域最新发展。 这本书面向的对象也不仅是学习相关专业的高校学生，还能够为研究人员和业界的技术人员提供稳妥的指导意见、提供解决问题的新鲜思路。("Deep Learning" covers the basic knowledge and principles of in-depth learning to th
Dy_ Win_ Approcach
- 动态窗口法（Dynamic window approach）是路径规划方法很重要的一种算法,主要是在速度空间中采样多组速度，并模拟机器人在这些速度下一定时间内的轨迹，在得到多组轨迹后，对这些轨迹进行评价，选出最优轨迹对应的速度来驱动机器人。(Dynamic window approach is an important algorithm for path planning. It mainly sampled multiple groups of velocities in the veloc