Abstract
In this paper, a new iterative winner-take-all (WTA) neural network is developed and analyzed. The proposed WTA neural net with one-layer structure is established under the concept of the statistical mean. For three typical distributions of initial activations, the convergence behaviors of the existing and the proposed WTA neural nets are evaluated by theoretical analyses and Monte Carlo simulations. We found that the suggested WTA neural network on average requires fewer than Log2 M iterations to complete a WTA process for the three distributed inputs, where M is the number of competitors. Furthermore, the fault tolerances of the iterative WTA nets are analyzed and simulated. From the view points of convergence speed, hardware complexity, and robustness to the errors, the proposed WTA is suitable for various applications.
| Original language | English |
|---|---|
| Pages (from-to) | 14-24 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Neural Networks |
| Volume | 6 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1995 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence
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A General Mean-Based Iterative Winner-Take-All Neural Network. / Yang, Jar-Ferr; Chen, Chi Ming; Wang, Wen Chung; Lee, Jau Yien.
In: IEEE Transactions on Neural Networks, Vol. 6, No. 1, 01.01.1995, p. 14-24.Research output: Contribution to journal › Article
TY - JOUR
T1 - A General Mean-Based Iterative Winner-Take-All Neural Network
AU - Yang, Jar-Ferr
AU - Chen, Chi Ming
AU - Wang, Wen Chung
AU - Lee, Jau Yien
PY - 1995/1/1
Y1 - 1995/1/1
N2 - In this paper, a new iterative winner-take-all (WTA) neural network is developed and analyzed. The proposed WTA neural net with one-layer structure is established under the concept of the statistical mean. For three typical distributions of initial activations, the convergence behaviors of the existing and the proposed WTA neural nets are evaluated by theoretical analyses and Monte Carlo simulations. We found that the suggested WTA neural network on average requires fewer than Log2 M iterations to complete a WTA process for the three distributed inputs, where M is the number of competitors. Furthermore, the fault tolerances of the iterative WTA nets are analyzed and simulated. From the view points of convergence speed, hardware complexity, and robustness to the errors, the proposed WTA is suitable for various applications.
AB - In this paper, a new iterative winner-take-all (WTA) neural network is developed and analyzed. The proposed WTA neural net with one-layer structure is established under the concept of the statistical mean. For three typical distributions of initial activations, the convergence behaviors of the existing and the proposed WTA neural nets are evaluated by theoretical analyses and Monte Carlo simulations. We found that the suggested WTA neural network on average requires fewer than Log2 M iterations to complete a WTA process for the three distributed inputs, where M is the number of competitors. Furthermore, the fault tolerances of the iterative WTA nets are analyzed and simulated. From the view points of convergence speed, hardware complexity, and robustness to the errors, the proposed WTA is suitable for various applications.
UR - http://www.scopus.com/inward/record.url?scp=0029207694&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0029207694&partnerID=8YFLogxK
U2 - 10.1109/72.363454
DO - 10.1109/72.363454
M3 - Article
VL - 6
SP - 14
EP - 24
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
SN - 2162-237X
IS - 1
ER -