文件名称:Extreme-nonlinear-function
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2013-06-26
- 文件大小：
- 98kb
- 下载次数：
- 0次
- 提 供 者：
- 万*
- 相关连接：
- 无
- 下载说明：
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介绍说明－－下载内容均来自于网络，请自行研究使用
用函数输入输出数据训练BP神经网络,使训练后的网络能够拟合非线性函数输出,保存训
练好的网络用于计算个体适应度值。根据非线性函数方程随机得到该函数的4000组输入输出数据,存储于data中,其中input为函数输入数据,output为函数对应输出数据,从中随机抽取3900组训练数据训练网络,100组测试数据测试网络拟合性能。最后保存训练好的网络。-With the function input and output data to train BP neural network, so that the trained network is capable of fitting nonlinear function of the output, save the trained network is used to calculate the value of individual fitness. According to the nonlinear functional equations randomly get this function up to 4000 input and output data, stored in the data, where the input as a function of the input data, output as a function of the corresponding output data from 3900 randomly selected set of training data to train the network, 100 sets of test data test Fitting network performance. Finally, save the trained network.
练好的网络用于计算个体适应度值。根据非线性函数方程随机得到该函数的4000组输入输出数据,存储于data中,其中input为函数输入数据,output为函数对应输出数据,从中随机抽取3900组训练数据训练网络,100组测试数据测试网络拟合性能。最后保存训练好的网络。-With the function input and output data to train BP neural network, so that the trained network is capable of fitting nonlinear function of the output, save the trained network is used to calculate the value of individual fitness. According to the nonlinear functional equations randomly get this function up to 4000 input and output data, stored in the data, where the input as a function of the input data, output as a function of the corresponding output data from 3900 randomly selected set of training data to train the network, 100 sets of test data test Fitting network performance. Finally, save the trained network.
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下载文件列表
Extreme nonlinear function\BP.m
..........................\Code.m
..........................\Cross.m
..........................\data.m
..........................\data.mat
..........................\fun.m
..........................\Genetic.m
..........................\Mutation.m
..........................\net.mat
..........................\Select.m
..........................\test.asv
..........................\test.m
Extreme nonlinear function