### Abstract

The objectives of this paper are to derive a momentum term in the Kohonen's self-organizing feature map algorithm theoretically and to show the effectiveness of the term by computer simulations. We will derive a self-organizing feature map algorithm having the momentum term through the following assumptions: 1) The cost function is E^{n} = Σ_{μ}
^{n} α^{n-μ} E_{μ}, where E_{μ} is the modified Lyapunov function originally proposed by Ritter and Schulten at the μ th learning time and α is the momentum coefficient. 2) The latest weights are assumed in calculating the cost function E^{n}. According to our simulations, it has shown that the momentum term in the self-organizing feature map can considerably contribute to the acceleration of the convergence.

Original language | English |
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Title of host publication | Proceedings of the International Joint Conference on Neural Networks |

Publisher | Publ by IEEE |

Pages | 467-470 |

Number of pages | 4 |

Volume | 1 |

ISBN (Print) | 0780314212, 9780780314214 |

Publication status | Published - 1993 |

Event | Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) - Nagoya, Jpn Duration: 1993 Oct 25 → 1993 Oct 29 |

### Other

Other | Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) |
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City | Nagoya, Jpn |

Period | 93/10/25 → 93/10/29 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*Proceedings of the International Joint Conference on Neural Networks*(Vol. 1, pp. 467-470). Publ by IEEE.

**Self-organizing feature map with a momentum term.** / Hagiwara, Masafumi.

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

*Proceedings of the International Joint Conference on Neural Networks.*vol. 1, Publ by IEEE, pp. 467-470, Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3), Nagoya, Jpn, 93/10/25.

}

TY - GEN

T1 - Self-organizing feature map with a momentum term

AU - Hagiwara, Masafumi

PY - 1993

Y1 - 1993

N2 - The objectives of this paper are to derive a momentum term in the Kohonen's self-organizing feature map algorithm theoretically and to show the effectiveness of the term by computer simulations. We will derive a self-organizing feature map algorithm having the momentum term through the following assumptions: 1) The cost function is En = Σμ n αn-μ Eμ, where Eμ is the modified Lyapunov function originally proposed by Ritter and Schulten at the μ th learning time and α is the momentum coefficient. 2) The latest weights are assumed in calculating the cost function En. According to our simulations, it has shown that the momentum term in the self-organizing feature map can considerably contribute to the acceleration of the convergence.

AB - The objectives of this paper are to derive a momentum term in the Kohonen's self-organizing feature map algorithm theoretically and to show the effectiveness of the term by computer simulations. We will derive a self-organizing feature map algorithm having the momentum term through the following assumptions: 1) The cost function is En = Σμ n αn-μ Eμ, where Eμ is the modified Lyapunov function originally proposed by Ritter and Schulten at the μ th learning time and α is the momentum coefficient. 2) The latest weights are assumed in calculating the cost function En. According to our simulations, it has shown that the momentum term in the self-organizing feature map can considerably contribute to the acceleration of the convergence.

UR - http://www.scopus.com/inward/record.url?scp=0027848472&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0027848472&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0027848472

SN - 0780314212

SN - 9780780314214

VL - 1

SP - 467

EP - 470

BT - Proceedings of the International Joint Conference on Neural Networks

PB - Publ by IEEE

ER -