文件名称:neural-network-matlab_m
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matlab神经网络原理与实例精解的matlab源代码，包括matlab快速入门、单层感知器、线性神经网络、BP神经网络、径向基函数网络、自组织竞争神经网络、随机神经网络等各章节源码，是学习神经网络的有力助手，里面包含matlab库函数工具箱的应用，也有手算代码。-matlab neural network theory and examples of fine solution matlab source code, including matlab QuickStart, single sensor, linear neural network, BP neural network, radial basis function network, since each chapter organizing competitive neural networks, Stochastic neural network sourceIt is a powerful assistant learning neural network, which contains the library functions matlab toolbox application, there are hand count code.
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Readme.txt
本书源文件
..........\第10章 随机神经网络
..........\...................\example10_1.m
..........\...................\sa_func.m
..........\...................\sa_tsp.m
..........\...................\tsp_len.m
..........\...................\tsp_new_path.m
..........\第11章 用GUI设计神经网络
..........\........................\fit_test.m
..........\........................\pr_test.m
..........\........................\som_test.m
..........\........................\stock1.mat
..........\第13章 神经网络应用实例
..........\.......................\BP神经网络实现图像压缩
..........\.......................\......................\block_divide.m
..........\.......................\......................\bp_imageCompress.m
..........\.......................\......................\bp_imageRecon.m
..........\.......................\......................\comp.mat
..........\.......................\......................\lena.bmp
..........\.......................\......................\re_divide.m
..........\.......................\Elman网络预测上证股市开盘价
..........\.......................\...........................\elman_stock.m
..........\.......................\...........................\elm_stock.mat
..........\.......................\...........................\elm_stockpredict.m
..........\.......................\基于BP网络的个人信贷信用评估
..........\.......................\............................\credit_class.m
..........\.......................\............................\german.data
..........\.......................\............................\german.data-numeric
..........\.......................\基于概率神经网络的手写体数字识别
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