Palmprint Recognition Using ICA Based on Winner-Take-All Network and Radial Basis Probabilistic Neural Network

2006 ◽  
pp. 216-221 ◽  
Author(s):  
Li Shang ◽  
De-Shuang Huang ◽  
Ji-Xiang Du ◽  
Zhi-Kai Huang
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Palmprint Recognition ◽  
Radial Basis ◽  
Winner Take All ◽  
Take All

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

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.

Improved winner-take-all neural network

1992 ◽  
Vol 28 (7) ◽  
pp. 662 ◽  
Author(s):  
J.-C. Yen ◽  
S. Chang
Keyword(s):  
Neural Network ◽  
Winner Take All ◽  
Take All

Automatic Detection of Arrow Annotation Overlays in Biomedical Images

2011 ◽  
Vol 6 (4) ◽  
pp. 23-41 ◽  
Author(s):  
Beibei Cheng ◽  
R. Joe Stanley ◽  
Soumya De ◽  
Sameer Antani ◽  
George R. Thoma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Computational Intelligence ◽  
Medical Images ◽  
Characteristic Curve ◽  
Data Set ◽  
Negative Correlation Learning ◽  
Artificial Neural ◽  
Winner Take All ◽  
Take All

Images in biomedical articles are often referenced for clinical decision support, educational purposes, and medical research. Authors-marked annotations such as text labels and symbols overlaid on these images are used to highlight regions of interest which are then referenced in the caption text or figure citations in the articles. Detecting and recognizing such symbols is valuable for improving biomedical information retrieval. In this research, image processing and computational intelligence methods are integrated for object segmentation and discrimination and applied to the problem of detecting arrows on these images. Evolving Artificial Neural Networks (EANNs) and Evolving Artificial Neural Network Ensembles (EANNEs) computational intelligence-based algorithms are developed to recognize overlays, specifically arrows, in medical images. For these discrimination techniques, EANNs use particle swarm optimization and genetic algorithm for artificial neural network (ANN) training, and EANNEs utilize the number of ANNs generated in an ensemble and negative correlation learning for neural network training based on averaging and Linear Vector Quantization (LVQ) winner-take-all approaches. Experiments performed on medical images from the imageCLEFmed’08 data set, yielded area under the receiver operating characteristic curve and precision/recall results as high as 0.988 and 0.928/0.973, respectively, using the EANNEs method with the winner-take-all approach.

A general mean-based iterative winner-take-all neural network

1995 ◽  
Vol 6 (1) ◽  
pp. 14-24 ◽  
Author(s):  
Jar-Ferr Yang ◽  
Chi-Ming Chen ◽  
Wen-Chung Wang ◽  
Jau-Yien Lee
Keyword(s):  
Neural Network ◽  
Winner Take All ◽  
Take All

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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