文件名称:BP-matlab
- 所属分类:
- 人工智能/神经网络/遗传算法
- 资源属性:
- [WORD]
- 上传时间:
- 2012-11-26
- 文件大小:
- 7kb
- 下载次数:
- 0次
- 提 供 者:
- ki***
- 相关连接:
- 无
- 下载说明:
- 别用迅雷下载,失败请重下,重下不扣分!
介绍说明--下载内容均来自于网络,请自行研究使用
基于C开发的三个隐层神经网络,包括
1)初始化权、阈值子程序;
2)第m个学习样本输入子程序;
3)第m个样本教师信号子程序;
4)隐层各单元输入、输出值子程序;
5)输出层各单元输入、输出值子程序;
6)输出层至隐层的一般化误差子程序;
7)隐层至输入层的一般化误差子程序;
8)输出层至第三隐层的权值调整、输出层阈值调整计算子程序;
9)第三隐层至第二隐层的权值调整、第三隐层阈值调整计算子程序;
10)第二隐层至第一隐层的权值调整、第二隐层阈值调整计算子程序;
11)第一隐层至输入层的权值调整、第一隐层阈值调整计算子程序;
12)N个样本的全局误差计算子程序。
-The three development based on C hidden layer neural networks, including
1) initialization, threshold subroutines,
2) first m a learning samples input subroutines,
3) the first m a sample teachers signal subroutines,
Each unit 4) hidden layer input and output value subroutines,
Each unit 5) output layer of input and output value subroutines,
6) output layer to hidden layer of generalization error subroutines,
7) hidden layer to input layers of general error subroutines,
8) output layer to the value of the hidden layer, the output layer threshold adjustment subroutines,
9) third hidden layer to the second hidden layer, the value of the hidden layer threshold adjustment subroutines,
10) second hidden layer to the value of the hidden layer, the second hidden layer threshold adjustment subroutines,
11) first hidden layer to the input value adjustment, the first hidden layer threshold adjustment subroutines,
12) N samples of global error subroutines.
1)初始化权、阈值子程序;
2)第m个学习样本输入子程序;
3)第m个样本教师信号子程序;
4)隐层各单元输入、输出值子程序;
5)输出层各单元输入、输出值子程序;
6)输出层至隐层的一般化误差子程序;
7)隐层至输入层的一般化误差子程序;
8)输出层至第三隐层的权值调整、输出层阈值调整计算子程序;
9)第三隐层至第二隐层的权值调整、第三隐层阈值调整计算子程序;
10)第二隐层至第一隐层的权值调整、第二隐层阈值调整计算子程序;
11)第一隐层至输入层的权值调整、第一隐层阈值调整计算子程序;
12)N个样本的全局误差计算子程序。
-The three development based on C hidden layer neural networks, including
1) initialization, threshold subroutines,
2) first m a learning samples input subroutines,
3) the first m a sample teachers signal subroutines,
Each unit 4) hidden layer input and output value subroutines,
Each unit 5) output layer of input and output value subroutines,
6) output layer to hidden layer of generalization error subroutines,
7) hidden layer to input layers of general error subroutines,
8) output layer to the value of the hidden layer, the output layer threshold adjustment subroutines,
9) third hidden layer to the second hidden layer, the value of the hidden layer threshold adjustment subroutines,
10) second hidden layer to the value of the hidden layer, the second hidden layer threshold adjustment subroutines,
11) first hidden layer to the input value adjustment, the first hidden layer threshold adjustment subroutines,
12) N samples of global error subroutines.
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