Poultry Skin Tumor Detection in Hyperspectral Images Using Radial Basis Probabilistic Neural Network

2006 ◽  
pp. 770-776 ◽  
Author(s):  
Intaek Kim ◽  
Chengzhe Xu ◽  
Moon S. Kim
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Tumor Detection ◽  
Skin Tumor ◽  
Hyperspectral Images ◽  
Radial Basis

Intelligent Control of Flocculation Process Based on Radial Basis Probabilistic Neural Network for Sewage Treatment

2010 ◽  
Vol 44-47 ◽  
pp. 3289-3293
Author(s):  
Jing Wen Tian ◽  
Mei Juan Gao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Process Model ◽  
Probabilistic Neural Network ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Least Square ◽  
Radial Basis ◽  
Flocculation Process

The flocculating process of sewage treatment is a complicated and nonlinear system, and it is very difficult to found the process model to describe it. The radial basis probabilistic neural network (RBPNN) has the ability of strong function approach and fast convergence. In this paper, an intelligent optimized control system based on radial basis probabilistic neural network is presented. We constructed the structure of radial basis probabilistic neural network that used for controlling the flocculation process, and adopt the K-Nearest Neighbor algorithm and least square method to train the network. We given the architecture of control system and analyzed the working process of system. In this system, the parameters of flocculation process were measured using sensors, and then the control system can control the flocculation process real-time. The system was used in the sewage treatment plant. The experimental results prove that this system is feasible.

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.

Palmprint recognition using FastICA algorithm and radial basis probabilistic neural network

2006 ◽  
Vol 69 (13-15) ◽  
pp. 1782-1786 ◽  
Author(s):  
Li Shang ◽  
De-Shuang Huang ◽  
Ji-Xiang Du ◽  
Chun-Hou Zheng
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Palmprint Recognition ◽  
Radial Basis ◽  
Fastica Algorithm
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