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.

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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