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《MATLAB神经网络原理与实例精解(附光盘)》首先简要介绍了MATLAB软件的使用和常用的内置函数，随后分门别类地介绍了BP网络、径向基网络、自组织网络、反馈网络等不同类型的神经网络-
Matlab neural network theory and examples of (CD-ROM) begins with a brief introduction to the use of MATLAB software and commonly used built-in function, then categorized the BP network, RBF network, self organization network, feedback network, such as different types of neural network
Matlab neural network theory and examples of (CD-ROM) begins with a brief introduction to the use of MATLAB software and commonly used built-in function, then categorized the BP network, RBF network, self organization network, feedback network, such as different types of neural network
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..........\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
..........\.......................\...........................\elm_stock.mat
..........\.......................\...........................\elm_stockpredict.m
..........\.......................\...........................\elman_stock.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
..........\.......................\................................\digital_pic
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