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MATLAB 代码
第1章 BP神经网络的数据分类——语音特征信号分类
第2章 BP神经网络的非线性系统建模——非线性函数拟合
第3章 遗传算法优化BP神经网络——非线性函数拟合
第4章 神经网络遗传算法函数极值寻优——非线性函数极值寻优
第5章 基于BP_Adaboost的强分类器设计——公司财务预警建模
第6章 PID神经元网络解耦控制算法——多变量系统控制
第7章 RBF网络的回归--非线性函数回归的实现
....等58章(MATLAB code
The first chapter is BP neural network data classification -- speech characteristic signal classification
The second chapter is the nonlinear system modeling of BP neural network nonlinear function fitting
The third chapter, genetic algorithm optimization BP neural network - nonlinear function fitting
The fourth chapter, neural network, genetic algorithm, function extreme value optimization nonlinear function extremum seeking
The fifth chapter is based on BP_Adaboost's strong classifier design -- the company financial early-warning model
The sixth chapter is PID neural network decoupling control algorithm multivariable system control
The seventh chapter is the regression of RBF network the realization of nonlinear function regression
.........the last is 58 chapters)
第1章 BP神经网络的数据分类——语音特征信号分类
第2章 BP神经网络的非线性系统建模——非线性函数拟合
第3章 遗传算法优化BP神经网络——非线性函数拟合
第4章 神经网络遗传算法函数极值寻优——非线性函数极值寻优
第5章 基于BP_Adaboost的强分类器设计——公司财务预警建模
第6章 PID神经元网络解耦控制算法——多变量系统控制
第7章 RBF网络的回归--非线性函数回归的实现
....等58章(MATLAB code
The first chapter is BP neural network data classification -- speech characteristic signal classification
The second chapter is the nonlinear system modeling of BP neural network nonlinear function fitting
The third chapter, genetic algorithm optimization BP neural network - nonlinear function fitting
The fourth chapter, neural network, genetic algorithm, function extreme value optimization nonlinear function extremum seeking
The fifth chapter is based on BP_Adaboost's strong classifier design -- the company financial early-warning model
The sixth chapter is PID neural network decoupling control algorithm multivariable system control
The seventh chapter is the regression of RBF network the realization of nonlinear function regression
.........the last is 58 chapters)
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下载文件列表
文件名 | 大小 | 更新时间 |
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代码/ | ||
代码/44层次分析/ | ||
代码/44层次分析/CENCI2.m | ||
代码/45灰色关联度度/ | ||
代码/45灰色关联度度/huiseguanliand1.m | ||
代码/46熵权法/ | ||
代码/46熵权法/sqf.m | ||
代码/47主成分分析/ | ||
代码/47主成分分析/pca.m | ||
代码/48主成分回归/ | ||
代码/48主成分回归/PCR_Demo.m | ||
代码/49偏最小二乘回归/ | ||
代码/49偏最小二乘回归/PLS.m | ||
代码/50逐步回归/ | ||
代码/50逐步回归/stepwise.zip | ||
代码/51模拟退火/ | ||
代码/51模拟退火/SA_GUI_Demo/ | ||
代码/51模拟退火/SA_GUI_Demo/fitness.m | ||
代码/51模拟退火/SA_GUI_Demo/html/ | ||
代码/51模拟退火/SA_GUI_Demo/html/SA_GUI_demo.html | ||
代码/51模拟退火/SA_GUI_Demo/html/SA_GUI_demo.png | ||
代码/51模拟退火/SA_GUI_Demo/html/SA_GUI_demo_01.png | ||
代码/51模拟退火/SA_GUI_Demo/html/SA_GUI_demo_02.png | ||
代码/51模拟退火/SA_GUI_Demo/html/SA_GUI_demo_03.png | ||
代码/51模拟退火/SA_GUI_Demo/html/SA_GUI_demo_04.png | ||
代码/51模拟退火/SA_GUI_Demo/html/SA_GUI_demo_05.png | ||
代码/51模拟退火/SA_GUI_Demo/main.m | ||
代码/51模拟退火/SA_TSP_Demo/ | ||
代码/51模拟退火/SA_TSP_Demo/Distance.m | ||
代码/51模拟退火/SA_TSP_Demo/DrawPath.m | ||
代码/51模拟退火/SA_TSP_Demo/Metropolis.m | ||
代码/51模拟退火/SA_TSP_Demo/NewAnswer.m | ||
代码/51模拟退火/SA_TSP_Demo/OutputPath.m | ||
代码/51模拟退火/SA_TSP_Demo/PathLength.m | ||
代码/51模拟退火/SA_TSP_Demo/html/ | ||
代码/51模拟退火/SA_TSP_Demo/html/main.html | ||
代码/51模拟退火/SA_TSP_Demo/html/main.png | ||
代码/51模拟退火/SA_TSP_Demo/html/main_01.png | ||
代码/51模拟退火/SA_TSP_Demo/html/main_02.png | ||
代码/51模拟退火/SA_TSP_Demo/html/main_03.png | ||
代码/51模拟退火/SA_TSP_Demo/main.m | ||
代码/52RBF | GRNN | PNN-神经网络/ |
代码/52RBF | GRNN | PNN-神经网络/ClassCode/ |
代码/52RBF | GRNN | PNN-神经网络/ClassCode/html/ |
代码/52RBF | GRNN | PNN-神经网络/ClassCode/html/main_GRNN_PNN.html |
代码/52RBF | GRNN | PNN-神经网络/ClassCode/html/main_GRNN_PNN.png |
代码/52RBF | GRNN | PNN-神经网络/ClassCode/html/main_GRNN_PNN_01.png |
代码/52RBF | GRNN | PNN-神经网络/ClassCode/html/main_GRNN_PNN_02.png |
代码/52RBF | GRNN | PNN-神经网络/ClassCode/html/main_GRNN_PNN_03.png |
代码/52RBF | GRNN | PNN-神经网络/ClassCode/html/main_RBF.html |
代码/52RBF | GRNN | PNN-神经网络/ClassCode/html/main_RBF.png |
代码/52RBF | GRNN | PNN-神经网络/ClassCode/html/main_RBF_01.png |
代码/52RBF | GRNN | PNN-神经网络/ClassCode/iris_data.mat |
代码/52RBF | GRNN | PNN-神经网络/ClassCode/main_GRNN_PNN.m |
代码/52RBF | GRNN | PNN-神经网络/ClassCode/main_RBF.m |
代码/52RBF | GRNN | PNN-神经网络/ClassCode/spectra_data.mat |
代码/53 竞争神经网络与SOM神经网络/ | ||
代码/53 竞争神经网络与SOM神经网络/ClassCode.rar | ||
代码/54蚁群算法tsp求解/ | ||
代码/54蚁群算法tsp求解/citys_data.mat | ||
代码/54蚁群算法tsp求解/html/ | ||
代码/54蚁群算法tsp求解/html/main.html | ||
代码/54蚁群算法tsp求解/html/main.png | ||
代码/54蚁群算法tsp求解/html/main_01.png | ||
代码/54蚁群算法tsp求解/html/main_02.png | ||
代码/54蚁群算法tsp求解/main.m | ||
代码/55灰色预测/ | ||
代码/55灰色预测/gm1_1benrentest.m | ||
代码/56 模糊综合评价/ | ||
代码/56 模糊综合评价/27194323mohu.rar | ||
代码/58曲线拟合/ | ||
代码/58曲线拟合/myfun.m | ||
代码/58曲线拟合/nihe.m | ||
代码/58曲线拟合/拟合图.png | ||
代码/chapter1/ | ||
代码/chapter1/BPDLX.m | ||
代码/chapter1/chapter1_1.asv | ||
代码/chapter1/chapter1_1.m | ||
代码/chapter1/data1.mat | ||
代码/chapter1/data2.mat | ||
代码/chapter1/data3.mat | ||
代码/chapter1/data4.mat | ||
代码/chapter10/ | ||
代码/chapter10/Readme.txt | ||
代码/chapter10/chapter10.m | ||
代码/chapter10/class.mat | ||
代码/chapter10/sim.mat | ||
代码/chapter10/stdlib.m | ||
代码/chapter10/test.m | ||
代码/chapter11/ | ||
代码/chapter11/Readme.txt | ||
代码/chapter11/city_location.mat | ||
代码/chapter11/diff_u.m | ||
代码/chapter11/energy.m | ||
代码/chapter11/main.m | ||
代码/chapter12/ | ||
代码/chapter12/Chapter_ClassifyRegressUsingLibsvm.m | ||
代码/chapter12/heart_scale.mat | ||
代码/chapter12/html/ | ||
代码/chapter12/html/Chapter_ClassifyRegressUsingLibsvm.html | ||
代码/chapter12/html/Chapter_ClassifyRegressUsingLibsvm.png | ||
代码/chapter12/html/Chapter_ClassifyRegressUsingLibsvm_01.png | ||
代码/chapter13/ | ||
代码/chapter13/Chapter_ModelDecryption.m | ||
代码/chapter13/heart_scale.mat | ||
代码/chapter13/html/ | ||
代码/chapter13/html/Chapter_ModelDecryption.html | ||
代码/chapter14/ | ||
代码/chapter14/chapter_WineClass.m | ||
代码/chapter14/chapter_WineClass.mat | ||
代码/chapter14/html/ | ||
代码/chapter14/html/chapter_WineClass.html | ||
代码/chapter14/html/chapter_WineClass.png | ||
代码/chapter14/html/chapter_WineClass_01.png | ||
代码/chapter14/html/chapter_WineClass_02.png | ||
代码/chapter14/html/chapter_WineClass_03.png | ||
代码/chapter15/ | ||
代码/chapter15/chapter_GA.m | ||
代码/chapter15/chapter_GridSearch.m | ||
代码/chapter15/chapter_PSO.m | ||
代码/chapter15/html/ | ||
代码/chapter15/html/chapter_GA.html | ||
代码/chapter15/html/chapter_GA.png | ||
代码/chapter15/html/chapter_GA_01.png | ||
代码/chapter15/html/chapter_GA_02.png | ||
代码/chapter15/html/chapter_GA_03.png | ||
代码/chapter15/html/chapter_GA_04.png | ||
代码/chapter15/html/chapter_GridSearch.html | ||
代码/chapter15/html/chapter_GridSearch.png | ||
代码/chapter15/html/chapter_GridSearch_01.png | ||
代码/chapter15/html/chapter_GridSearch_02.png | ||
代码/chapter15/html/chapter_GridSearch_03.png | ||
代码/chapter15/html/chapter_GridSearch_04.png | ||
代码/chapter15/html/chapter_GridSearch_05.png | ||
代码/chapter15/html/chapter_GridSearch_06.png | ||
代码/chapter15/html/chapter_GridSearch_07.png | ||
代码/chapter15/html/chapter_PSO.html | ||
代码/chapter15/html/chapter_PSO.png | ||
代码/chapter15/html/chapter_PSO_01.png | ||
代码/chapter15/html/chapter_PSO_02.png | ||
代码/chapter15/html/chapter_PSO_03.png | ||
代码/chapter15/html/chapter_PSO_04.png | ||
代码/chapter15/wine.mat | ||
代码/chapter16/ | ||
代码/chapter16/chapter_sh.m | ||
代码/chapter16/chapter_sh.mat | ||
代码/chapter16/html/ | ||
代码/chapter16/html/chapter_sh.html | ||
代码/chapter16/html/chapter_sh.png | ||
代码/chapter16/html/chapter_sh_01.png | ||
代码/chapter16/html/chapter_sh_02.png | ||
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代码/chapter16/html/chapter_sh_08.png | ||
代码/chapter16/html/chapter_sh_09.png | ||
代码/chapter17/ | ||
代码/chapter17/FIG_D.m | ||
代码/chapter17/chapter_FIGsh.m | ||
代码/chapter17/chapter_sh.mat | ||
代码/chapter17/html/ | ||
代码/chapter17/html/chapter_FIGsh.html | ||
代码/chapter17/html/chapter_FIGsh.png |
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