搜索资源列表
CHAPT4
- 神经网络模式识别及其实现，第四章。 内含：ALOPEX和BACKPROP程序。-pattern recognition and neural network to achieve, the fourth chapter. Intron : ALOPEX BACKPROP and procedures.
ALOPEX算法(即模式提取算法)源程序ALOPEX
- 即模式提取算法源程序-that model extraction algorithm source
ALOPEX
- 高人用C＋＋写的ALOPEX(即模式提取算法)实现程序，程序相当精炼，值得大家研究！-expert C + + to write the ALOPEX (model extraction algorithm) procedures, procedures considerable refining, we should study!
ALOPEX.ZIP
- ALOPEX算法(即模式提取算法)源程序-ALOPEX algorithm (that is, model extraction algorithm) source
Neural_Network_Code_CHAPT4
- ALOPEX算法:它把神经网络的学习过程看作最优化问题的随机并行算法。-ALOPEX algorithm : it neural network learning process optimization as a random parallel algorithm.
ALOPEX
- Alopex算法-Alopex algorithm
ALOPEX
- 模式提取算法，它把神经网络的学习过程看作,最优化问题的随机并行算法。
ALOPEX算法(即模式提取算法)源程序ALOPEX
- 即模式提取算法源程序-that model extraction algorithm source
CHAPT4
- 神经网络模式识别及其实现，第四章。 内含：ALOPEX和BACKPROP程序。-pattern recognition and neural network to achieve, the fourth chapter. Intron : ALOPEX BACKPROP and procedures.
ALOPEX
- 高人用C＋＋写的ALOPEX(即模式提取算法)实现程序，程序相当精炼，值得大家研究！-expert C++ to write the ALOPEX (model extraction algorithm) procedures, procedures considerable refining, we should study!
ALOPEX.ZIP
- ALOPEX算法(即模式提取算法)源程序-ALOPEX algorithm (that is, model extraction algorithm) source
Neural_Network_Code_CHAPT4
- ALOPEX算法:它把神经网络的学习过程看作最优化问题的随机并行算法。-ALOPEX algorithm : it neural network learning process optimization as a random parallel algorithm.
ALOPEX
- 模式提取算法，它把神经网络的学习过程看作,最优化问题的随机并行算法。-Model extraction algorithm, which the neural network as a learning process, the stochastic optimization problem parallel algorithm.
ALOPEX
- 用神经网络bp动量算法实现模式识别，用C++实现-Momentum with neural network pattern recognition algorithm, using C++ to achieve
rithm
- 一种自适应变步长的Alopex算法An adaptive variable step of the Alopex algorithm-An adaptive variable step of the Alopex algorithm
基于alopex的粒子群算法
- 该算法通过基于Alopex的粒子群优化算法，结合神经网络计算，恰当地对所给数据进行聚类并进行拟合，从何达到了很好的分类和优化效果(Based on the Alopex particle swarm optimization algorithm and neural network calculation, the algorithm can properly cluster and fit the data, which can ac