### 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 |
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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) |
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City | Nagoya, Jpn |

Period | 10/25/93 → 10/29/93 |

### Fingerprint

### ASJC Scopus subject areas

- Software

### Cite this

*Proceedings of the International Joint Conference on Neural Networks*(Vol. 2, pp. 1520-1523). Publ by IEEE.

**Neurons with an immunological rejective function and their application to scheduling problems.** / Arizono, Ikuo; Hara, Shinji; Ohta, Hiroshi.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the International Joint Conference on Neural Networks.*vol. 2, Publ by IEEE, pp. 1520-1523, Proceedings of 1993 International Joint Conference on Neural Networks. Part 2 (of 3), Nagoya, Jpn, 10/25/93.

}

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 -