Abstract
A maximum neuron model is proposed in order to force the state of the system to converge to the solution in neural dynamics. The state of the system is always forced in a solution domain. The artificial maximum neural network is used for the module orientation problem and the bipartite subgraph problem. The usefulness of the maximum neural network is empirically demonstrated by simulating randomly generated massive nstances (examples) in both problems. In randomly generated more than one thousand instances our system always converges to the solution within one hundred iteration steps regardless of the problem size. Our simulation results show the effectiveness of our algorithms and support our claim that one class of NP-complete problems may be solvable in a polynomial time.
| Original language | English |
|---|---|
| Pages (from-to) | 243-251 |
| Number of pages | 9 |
| Journal | Biological Cybernetics |
| Volume | 67 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1992 Jul |
| Externally published | Yes |
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ASJC Scopus subject areas
- Biophysics
Cite this
An artificial maximum neural network : a winner-take-all neuron model forcing the state of the system in a solution domain. / Takefuji, Yoshiyasu; Lee, Kuo Chun; Also, Hideo.
In: Biological Cybernetics, Vol. 67, No. 3, 07.1992, p. 243-251.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - An artificial maximum neural network
T2 - a winner-take-all neuron model forcing the state of the system in a solution domain
AU - Takefuji, Yoshiyasu
AU - Lee, Kuo Chun
AU - Also, Hideo
PY - 1992/7
Y1 - 1992/7
N2 - A maximum neuron model is proposed in order to force the state of the system to converge to the solution in neural dynamics. The state of the system is always forced in a solution domain. The artificial maximum neural network is used for the module orientation problem and the bipartite subgraph problem. The usefulness of the maximum neural network is empirically demonstrated by simulating randomly generated massive nstances (examples) in both problems. In randomly generated more than one thousand instances our system always converges to the solution within one hundred iteration steps regardless of the problem size. Our simulation results show the effectiveness of our algorithms and support our claim that one class of NP-complete problems may be solvable in a polynomial time.
AB - A maximum neuron model is proposed in order to force the state of the system to converge to the solution in neural dynamics. The state of the system is always forced in a solution domain. The artificial maximum neural network is used for the module orientation problem and the bipartite subgraph problem. The usefulness of the maximum neural network is empirically demonstrated by simulating randomly generated massive nstances (examples) in both problems. In randomly generated more than one thousand instances our system always converges to the solution within one hundred iteration steps regardless of the problem size. Our simulation results show the effectiveness of our algorithms and support our claim that one class of NP-complete problems may be solvable in a polynomial time.
UR - http://www.scopus.com/inward/record.url?scp=0344194652&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0344194652&partnerID=8YFLogxK
U2 - 10.1007/BF00204397
DO - 10.1007/BF00204397
M3 - Article
AN - SCOPUS:0344194652
VL - 67
SP - 243
EP - 251
JO - Biological Cybernetics
JF - Biological Cybernetics
SN - 0340-1200
IS - 3
ER -