Description: ADIAL Basis Function (RBF) networks were introduced
into the neural network literature by Broomhead and
Lowe [1], which are motivated by observation on the local
response in biologic neurons. Due to their better
approximation capabilities, simpler network structures and
faster learning algorithms, RBF networks have been widely applied in many science and engineering fields. RBF network is three layers feedback network, where each hidden unit implements a radial activation function and each output unit implements a weighted sum of hidden units’ outputs.
File list (Check if you may need any files):
新建 WinRAR ZIP 档案文件
........................\bisheji
........................\.......\bisheji.dsp
........................\.......\bisheji.dsw
........................\.......\bisheji.ncb
........................\.......\bisheji.opt
........................\.......\bisheji.plg
........................\.......\bp_rbf.h
........................\.......\bp结果
........................\.......\......\权值.txt
........................\.......\......\误差变化序列.txt
........................\.......\Debug
........................\.......\mainApp.cpp
........................\.......\matrix_op.h
........................\.......\rbf.cpp
........................\.......\rbf结果
........................\.......\.......\权值.txt
........................\.......\.......\误差.txt
........................\.......\study.txt
........................\.......\test.txt
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