### 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 |

ISBN (Print) | 0780314212, 9780780314214 |

Publication status | Published - 1993 Dec 1 |

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

### Publication series

Name | Proceedings of the International Joint Conference on Neural Networks |
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Volume | 1 |

### 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

- Software
- Artificial Intelligence

### Cite this

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