资源列表
Neural-network-algorithm
- 神经网络算法源程序，包含8种神经网络,在VC环境中运行-Neural network algorithm source code, contains 8 kinds of neural networks
a-nerve-network-of-matlab
- matlab神经网络一例，运用比例共轭梯度动量算法来训练 BP网络 。-matlab neural network case, the use of the momentum ratio of conjugate gradient algorithm to train the BP network.
GA-optimize--NN
- 代码是为一个19输入变量，1个输出变量情况下的非线性回归而设计的，如果要应用于其它情况，只需改动编解码函数即可。-Code is a 19 input variables, a case of output variables in nonlinear regression and design, if used in other cases, simply encoding and decoding functions can be changed.
Double-Inverted-Pendulum
- 基于BP神经网络的二级倒立摆控制器选择小车的位移和速度及两个摆杆偏离铅锤线的角度和角速度为输入数据。并经训练可得到一个神经网络控制器。-The controller of the double inverted pendulum based on the BP neural network chooses displacement and speed of the car, the angle between the two pendulums bar and vertical line and
ISODATA
- VC下编写的基于ISODATA算法的模式识别程序，内含配套的实验数据，注释很清楚，数据结构完整，很合适初学模式识别的朋友借鉴-VC prepared ISODATA algorithm based on pattern recognition program that includes matching the experimental data, a comment is clear, the data integrity of the structure, it is an appropri
A-star-search-algorithm
- 这是一个关于a星算法的文档，而且还是英文的，灰常考你英语水平-a star
BP-
- 基于改进BP 神经网络的移动机器人寻线控制神经网络系统具有自学习和自适应的能力，同时有很强的容错性和鲁棒性，适用于处理难于语言化的模式信息。为使移动机器人沿地面标志线自主运动，采用CCD 图像传感器与PC/104总线相结合的硬件系统，运用神经网络的模式识别功能，实现了机器人的寻线控制，实验结果表明该方法是可行的，能有效地提高移动机器人对环境的适应性和其智能化水平。- Neural Network has the ability of self-learning and self-adaptati
P020100707545239496937
- 这是一篇讨论车载导航的滤波算法的研究，基于matlab实现仿真-This is a discussion of vehicle navigation filtering algorithm, based on the realization of simulation matlab
GOFuzzyv1.0
- 能够实现模糊聚类,
gaosisaideer
- 数值计算 高斯赛德尔迭代法 用C++语言描述-High Sisaideer numerical iterative method described by C++ Language
genetic-algorithms
- 介绍了利用遗传算法，优化稀布阵列的阵元的排布-Introduced the use of genetic algorithms and particle swarm optimization, optimization thinned array element array arranged
RMU900P_datasheet
- UHF超高频915MHz射频识别模块的资料，UHF频段的射频识别模块和资料都比较少，这个是难得的资料-UHF 915MHz UHF radio frequency identification module information, UHF band radio frequency identification module and information than less information on this is a rare