资源列表
GA
- 简单介绍遗传算法入门教程 非常适用初学者-Briefly introduce the genetic algorithm is suitable for beginners Tutorial
nn_5
- 以bp模擬碗公!!並加以訓練然後再佳以模擬內容簡單歡迎下載-Bp simulation bowl to the public!! And good training and then to simulate the contents of a simple welcome to download
rbf_nn
- 類神經網路的RBF這對於任何研究都非常之友幫助歡迎下載內有說明-The RBF neural network for any research to help the Friends are welcome to download, there are notes
parkingproblem
- 轮式移动机器人的镇定方法，从日常生活中的经验总结的方法-Stabilization of wheeled mobile robot methods, from the everyday experience of the methods
ANN-moving-robot
- 运用人工神经网络和遗传算法实现机器人程序能够自动排除地图中的障碍物。-The use of artificial neural networks and genetic algorithm procedures robot can automatically rule out the possibility of obstructions map.
SourceCode
- 本程序所采用的启发函数,本人不能证明它能解决任何的八数码难题,如果对于较难的问题会产生很多节点,有可能会使空间不足,所以尽量不要出太难的问题难为它.希望广大的人工智能爱好者能写出更好的启发函数.-This procedure adopted by the heuristic function, I can not prove that it can resolve any of the eight digital dilemma, if the more difficult the proble
getgrad
- Produces a matrix of derivatives of network output w.r.t. ％ each network weight for use in the functions NNPRUNE and NNFPE.-Produces a matrix of derivatives of network output wrt each network weight for use in the functions NNPRUNE and NNFPE.
marq
- ％ Train a two layer neural network with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is possible to use regularization by ％ weight decay. Also pruned (ie. not fully connected) networks can ％ be trained. ％ ％ Given a set of cor
nnfpe
- This function calculates Akaike s final prediction error ％ estimate of the average generalization error for network ％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive ％ counterparts. ％ ％ [FPE,deff,varest,H] = nnfpe(method,Ne
ants
- 蚂蚁算法伪码 蚂蚁算法解决TSP问题的C++程序-Ant Algorithm pseudo-code ant algorithm to solve TSP problems C++ Procedures
PSO
- 粒子群算法，通过理解，自己写出的原代码，供大家参考-Particle swarm optimization, through understanding, to write their own original code, for your reference
bp
- 这是我的一个作业，本人初学神经网络，希望大家多提些意见-This is one of my operations, I am learning neural networks, hope that we talk too much about these views