### Abstract

Several neuron models and artificial neural networks have been intensively studied since McCulloch and Pitts proposed the simplified neuron model in 1943. In this paper a generalized maximum neural network for parallel computing is introduced to solve the module orientation problem which belongs to the class of NP-complete problems. The goal of the module orientation problem in VLSI circuits or printed circuit boards is to minimize the total wire length by flipping each module with respect to its vertical and/or horizontal axes of symmetry. The circuit diagram of the generalized maximum neural network is shown and compared with the best known algorithm proposed by Libeskind-Hadas and Liu. The theoretical/empirical convergence analysis is discussed where a massive number of simulation runs were performed using more than one thousand instances. As far as we have observed the behavior of the proposed system, it converges within O(1) time regardless of the problem size and it performs better than the best known algorithm in terms of the solution quality and the computation time.

Original language | English |
---|---|

Pages (from-to) | 331-355 |

Number of pages | 25 |

Journal | International Journal of Electronics |

Volume | 72 |

Issue number | 3 |

Publication status | Published - 1992 Mar |

Externally published | Yes |

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### ASJC Scopus subject areas

- Electrical and Electronic Engineering

### Cite this

*International Journal of Electronics*,

*72*(3), 331-355.

**Generalized maximum neural network for the module orientation problem.** / Lee, Kuo Chun; Takefuji, Yoshiyasu.

Research output: Contribution to journal › Article

*International Journal of Electronics*, vol. 72, no. 3, pp. 331-355.

}

TY - JOUR

T1 - Generalized maximum neural network for the module orientation problem

AU - Lee, Kuo Chun

AU - Takefuji, Yoshiyasu

PY - 1992/3

Y1 - 1992/3

N2 - Several neuron models and artificial neural networks have been intensively studied since McCulloch and Pitts proposed the simplified neuron model in 1943. In this paper a generalized maximum neural network for parallel computing is introduced to solve the module orientation problem which belongs to the class of NP-complete problems. The goal of the module orientation problem in VLSI circuits or printed circuit boards is to minimize the total wire length by flipping each module with respect to its vertical and/or horizontal axes of symmetry. The circuit diagram of the generalized maximum neural network is shown and compared with the best known algorithm proposed by Libeskind-Hadas and Liu. The theoretical/empirical convergence analysis is discussed where a massive number of simulation runs were performed using more than one thousand instances. As far as we have observed the behavior of the proposed system, it converges within O(1) time regardless of the problem size and it performs better than the best known algorithm in terms of the solution quality and the computation time.

AB - Several neuron models and artificial neural networks have been intensively studied since McCulloch and Pitts proposed the simplified neuron model in 1943. In this paper a generalized maximum neural network for parallel computing is introduced to solve the module orientation problem which belongs to the class of NP-complete problems. The goal of the module orientation problem in VLSI circuits or printed circuit boards is to minimize the total wire length by flipping each module with respect to its vertical and/or horizontal axes of symmetry. The circuit diagram of the generalized maximum neural network is shown and compared with the best known algorithm proposed by Libeskind-Hadas and Liu. The theoretical/empirical convergence analysis is discussed where a massive number of simulation runs were performed using more than one thousand instances. As far as we have observed the behavior of the proposed system, it converges within O(1) time regardless of the problem size and it performs better than the best known algorithm in terms of the solution quality and the computation time.

UR - http://www.scopus.com/inward/record.url?scp=0026837860&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0026837860&partnerID=8YFLogxK

M3 - Article

VL - 72

SP - 331

EP - 355

JO - International Journal of Electronics

JF - International Journal of Electronics

SN - 0020-7217

IS - 3

ER -