### 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 the 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), where E(μ) is the modified Lyapunov function originally proposed by Ritter and Schulten at the μth learning time and Q 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 |
---|---|

Pages (from-to) | 71-81 |

Number of pages | 11 |

Journal | Neurocomputing |

Volume | 10 |

Issue number | 1 |

DOIs | |

Publication status | Published - 1996 Jan |

### Fingerprint

### Keywords

- Momentum term
- Self-organizing feature map

### ASJC Scopus subject areas

- Artificial Intelligence
- Cellular and Molecular Neuroscience

### Cite this

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

Research output: Contribution to journal › Article

*Neurocomputing*, vol. 10, no. 1, pp. 71-81. https://doi.org/10.1016/0925-2312(94)00056-5

}

TY - JOUR

T1 - Self-organizing feature map with a momentum term

AU - Hagiwara, Masafumi

PY - 1996/1

Y1 - 1996/1

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 the 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), where E(μ) is the modified Lyapunov function originally proposed by Ritter and Schulten at the μth learning time and Q 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.

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 the 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), where E(μ) is the modified Lyapunov function originally proposed by Ritter and Schulten at the μth learning time and Q 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.

KW - Momentum term

KW - Self-organizing feature map

UR - http://www.scopus.com/inward/record.url?scp=0030050210&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030050210&partnerID=8YFLogxK

U2 - 10.1016/0925-2312(94)00056-5

DO - 10.1016/0925-2312(94)00056-5

M3 - Article

AN - SCOPUS:0030050210

VL - 10

SP - 71

EP - 81

JO - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

IS - 1

ER -