scholarly journals Comparison of Multi Layered Percepton and Radial Basis Function Classification Performance of Lung Cancer Data

2020 ◽  
Vol 1471 ◽  
pp. 012043
Author(s):  
Yessi Jusman ◽  
Zul Indra ◽  
Roni Salambue ◽  
Siti Nurul Aqmariah Mohd Kanafiah ◽  
Muhammad Ahdan Fawwaz Nurkholid
Keyword(s):  
Lung Cancer ◽  
Radial Basis Function ◽  
Basis Function ◽  
Classification Performance ◽  
Cancer Data ◽  
Lung Cancer Data ◽  
Radial Basis

APPLICATION OF GENERALIZED RADIAL BASIS FUNCTION NETWORKS TO RECOGNITION OF RADAR TARGETS

1999 ◽  
Vol 13 (06) ◽  
pp. 945-962 ◽  
Author(s):  
DE-SHUANG HUANG
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Classification Performance ◽  
Kernel Functions ◽  
Radial Basis Function Networks ◽  
One Dimensional ◽  
Parzen Window ◽  
Window Functions ◽  
Radial Basis ◽  
Radar Targets

This paper extends general radial basis function networks (RBFN) with Gaussian kernel functions to generalized radial basis function networks (GRBFN) with Parzen window functions, and discusses applying the GRBFNs to recognition of radar targets. The equivalence between the RBFN classifiers (RBFNC) with outer-supervised signals of 0 or 1 and the estimate of Parzen windowed probabilistic density is proved. It is pointed out that the I/O functions of the hidden units in the RBFNC can be extended to general Parzen window functions (or called as potential functions). We present using recursive least square-backpropagation (RLS–BP) learning algorithm to train the GRBFNCs to classify five types of radar targets by means of their one-dimensional cross profiles. The concepts about the rate of recognition and confidence in the process of testing classification performance of the GRBFNCs are introduced. Six generalized kernel functions such as Gaussian, Double-Exponential, Triangle, Hyperbolic, Sinc and Cauchy, are used as the hidden I/O functions of the RBFNCs, and the classification performance of corresponding GRBFNCs for classifying one-dimensional cross profiles of radar targets is discussed.

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