资源列表
TFromthedistah
- 这是一个模拟路由器的距离矢量路由算法， Dijjkstra算法是核心。-This is a simulated router distance vector routing algorithm Dijjkstra algorithm is the core.
Thelloworldh
- 这是vb6.0用于操作淘宝appi的一个例子，函数书写非常简便。 -Vb6.0 for the operation of Taobao appi an example, the function of writing is very simple.
Tmullti_Langgh
- 这是多语言菜单，可实现不同的国家选选用不同的语言菜单。供学习开发使用。 -This is a multi-language menu, different national election to choose a different language menu. For learning and developing use.
0NMEEA0183pri1
- 0183协议的解析程序源码的源代码，能能用在定位，导航，监控等方面 -0183 agreement analytic program source code, source code, can use in positioning, navigation, surveillance, etc.
MFFuuzzyPIDa
- 运用matlab程序源码，进进行模糊PID设计 -Use of matlab program source code, into the fuzzy PID design
Nnnrrfxxfce
- 牛顿法计算方程，能计算非线性方程/方程程组，有需要的能试试，主要是数学方法的实现 -Newton method to calculate the equation to calculate the nonlinear equation/equation of equations, needs to try, mainly the implementation of the mathematical methods
Am4k20powwerd
- ofdm系统中功率自适应分配算法研究的matlabb源代码（k＝20） ， -ofdm systems, adaptive power allocation algorithm research matlabb source (k = 20),
Azishiyingsuad
- 自适应算术编码程序，适合初学VVC的兄弟姐妹。比较具有实用价值。 -Adaptive arithmetic coding procedures, suitable for beginners VVC brothers and sisters. More practical value.
Tsteepest_desh
- 这个Matlab程序实现最速下降算法。最速下降法是一种最基本的算法，它在最优化方法中占有重要地位.最速下降法的优点是工作量小，存储储变量较少,初始点要求不高；缺点是收敛慢，最速下降法适用于寻优过程的前期迭代或作为间插步骤，当接近极值点时，宜选用别种收敛快的算法. -The Matlab program to achieve the steepest descent algorithm. Steepest descent method is a basic algorithm, it occu
TWavvelet22Dh
- 此源代码,是一个实现离散余弦变换的程序源码，包含普通的离散余弦变变换与整数离散余弦变换，能轻松的移植到你的程序源码中 -This source code is an implementation of the program source code of the discrete cosine transform, discrete cosine ordinary variable transformation and integer discrete cosine transform c
LRTDD2023LVGGC
- 液晶LCD显示出来器驱动driver板源程序序源码(RTD2023LVGA2) -LCD display, out drive the driver board source sequence source (RTD2023LVGA2)
Mmutti-agentsu
- 多智能体工具包，可直接用来进行行多智能体强化学习算法设计与仿真 -Multi-agent toolkit, can be directly used for design and simulation of the line multi-agent reinforcement learning algorithm