Abstract
If the energy function of an artificial neural network system is expressed in quadratic form based on the objective function and constraints of a combinatorial optimization problem, it is possible to get a solution using the equations of system dynamics; a special algorithm is not required. However, even though the subenergy functions based on the constraints can be expressed in quadratic form, minimizing the subenergy functions does not necessarily give a feasible solution satisfying the constraints of the optimization problem. This shows that neural network systems have a limit in their applicability. In this paper we propose new neurons, with an immunological rejective function, so as to extend the applicability of neural network system. These neurons have the function of rejecting the violation of constraints. We apply these neurons to scheduling problems and discuss their capabilities.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
| Editors | Anon |
| Publisher | Publ by IEEE |
| Pages | 1520-1523 |
| Number of pages | 4 |
| Volume | 2 |
| ISBN (Print) | 0780314212 |
| Publication status | Published - 1993 |
| Externally published | Yes |
| Event | Proceedings of 1993 International Joint Conference on Neural Networks. Part 2 (of 3) - Nagoya, Jpn Duration: Oct 25 1993 → Oct 29 1993 |
Other
| Other | Proceedings of 1993 International Joint Conference on Neural Networks. Part 2 (of 3) |
|---|---|
| City | Nagoya, Jpn |
| Period | 10/25/93 → 10/29/93 |
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ASJC Scopus subject areas
- Software
Cite this
Neurons with an immunological rejective function and their application to scheduling problems. / Arizono, Ikuo; Hara, Shinji; Ohta, Hiroshi.
Proceedings of the International Joint Conference on Neural Networks. ed. / Anon. Vol. 2 Publ by IEEE, 1993. p. 1520-1523.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Neurons with an immunological rejective function and their application to scheduling problems
AU - Arizono, Ikuo
AU - Hara, Shinji
AU - Ohta, Hiroshi
PY - 1993
Y1 - 1993
N2 - If the energy function of an artificial neural network system is expressed in quadratic form based on the objective function and constraints of a combinatorial optimization problem, it is possible to get a solution using the equations of system dynamics; a special algorithm is not required. However, even though the subenergy functions based on the constraints can be expressed in quadratic form, minimizing the subenergy functions does not necessarily give a feasible solution satisfying the constraints of the optimization problem. This shows that neural network systems have a limit in their applicability. In this paper we propose new neurons, with an immunological rejective function, so as to extend the applicability of neural network system. These neurons have the function of rejecting the violation of constraints. We apply these neurons to scheduling problems and discuss their capabilities.
AB - If the energy function of an artificial neural network system is expressed in quadratic form based on the objective function and constraints of a combinatorial optimization problem, it is possible to get a solution using the equations of system dynamics; a special algorithm is not required. However, even though the subenergy functions based on the constraints can be expressed in quadratic form, minimizing the subenergy functions does not necessarily give a feasible solution satisfying the constraints of the optimization problem. This shows that neural network systems have a limit in their applicability. In this paper we propose new neurons, with an immunological rejective function, so as to extend the applicability of neural network system. These neurons have the function of rejecting the violation of constraints. We apply these neurons to scheduling problems and discuss their capabilities.
UR - http://www.scopus.com/inward/record.url?scp=0027814459&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0027814459&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0027814459
SN - 0780314212
VL - 2
SP - 1520
EP - 1523
BT - Proceedings of the International Joint Conference on Neural Networks
A2 - Anon, null
PB - Publ by IEEE
ER -