Structure Optimization Algorithm for Radial Basis Probabilistic Neural Networks Based on the Moving Median Center Hyperspheres Algorithm

2009 ◽  
pp. 136-143
Author(s):  
Ji-Xiang Du ◽  
Chuan-Min Zhai
Keyword(s):  
Neural Networks ◽  
Optimization Algorithm ◽  
Structure Optimization ◽  
Probabilistic Neural Networks ◽  
Radial Basis

A novel full structure optimization algorithm for radial basis probabilistic neural networks

2006 ◽  
Vol 70 (1-3) ◽  
pp. 592-596 ◽  
Author(s):  
Ji-Xiang Du ◽  
De-Shuang Huang ◽  
Guo-Jun Zhang ◽  
Zeng-Fu Wang
Keyword(s):  
Neural Networks ◽  
Optimization Algorithm ◽  
Structure Optimization ◽  
Probabilistic Neural Networks ◽  
Radial Basis ◽  
Full Structure

RADIAL BASIS PROBABILISTIC NEURAL NETWORKS: MODEL AND APPLICATION

1999 ◽  
Vol 13 (07) ◽  
pp. 1083-1101 ◽  
Author(s):  
DE-SHUANG HUANG
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Probabilistic Neural Network ◽  
Radial Basis Function Networks ◽  
Probabilistic Neural Networks ◽  
Huge Amount ◽  
Training Time ◽  
One Dimensional ◽  
Radial Basis

This paper investigates the capabilities of radial basis function networks (RBFN) and kernel neural networks (KNN), i.e. a specific probabilistic neural networks (PNN), and studies their similarities and differences. In order to avoid the huge amount of hidden units of the KNNs (or PNNs) and reduce the training time for the RBFNs, this paper proposes a new feedforward neural network model referred to as radial basis probabilistic neural network (RBPNN). This new network model inherits the merits of the two old odels to a great extent, and avoids their defects in some ways. Finally, we apply this new RBPNN to the recognition of one-dimensional cross-images of radar targets (five kinds of aircrafts), and the experimental results are given and discussed.

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