### Abstract

Original language | Japanese |
---|---|

Pages (from-to) | 18-23 |

Number of pages | 6 |

Journal | Kagaku Kogaku Ronbunshu |

Volume | 24 |

Issue number | 1 |

DOIs | |

Publication status | Published - Jan 10 1998 |

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*Kagaku Kogaku Ronbunshu*,

*24*(1), 18-23. https://doi.org/10.1252/kakoronbunshu.24.18

**ファジィニューラルネットワークによる内装タイルの官能評価モデリング.** / 花井泰三; 安藤一徳; 野口英樹; 本多裕之; 高井智代; 古橋武; 内川嘉樹; 小林猛.

Research output: Contribution to journal › Article

*Kagaku Kogaku Ronbunshu*, vol. 24, no. 1, pp. 18-23. https://doi.org/10.1252/kakoronbunshu.24.18

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TY - JOUR

T1 - ファジィニューラルネットワークによる内装タイルの官能評価モデリング

AU - 花井, 泰三

AU - 安藤, 一徳

AU - 野口, 英樹

AU - 本多, 裕之

AU - 高井, 智代

AU - 古橋, 武

AU - 内川, 嘉樹

AU - 小林, 猛

PY - 1998/1/10

Y1 - 1998/1/10

N2 - In order to construct a novel design method for interior tiles, the relationship between image words (sensory evaluation) and total evaluation for interior tiles was studied. At first, the model for estimation of the total evaluation from image words was constructed using a fuzzy neural network (FNN) and MRA (multi regression analysis). The FNN model could estimate the total evaluation more correctly than the MRA model. Secondly, a FNN model for the estimation of the total evaluation from analytical data for interior tiles was constructed. This model was less accurate than the FNN model from image words, but could estimate within 10% errors. Lastly, in order to improve accuracy of the estimation, a FNN model for estimation of the total evaluation from both analytical data and image words was constructed. This model showed the highest accuracy among all models. "<I>Jyouhinna-gehinna", "<I>shareta-yabona" among image words and "average value of green color", "arithmetical mean deviation", "peak count" among analytical data were needed to construct the model for estimation of the total evaluation.

AB - In order to construct a novel design method for interior tiles, the relationship between image words (sensory evaluation) and total evaluation for interior tiles was studied. At first, the model for estimation of the total evaluation from image words was constructed using a fuzzy neural network (FNN) and MRA (multi regression analysis). The FNN model could estimate the total evaluation more correctly than the MRA model. Secondly, a FNN model for the estimation of the total evaluation from analytical data for interior tiles was constructed. This model was less accurate than the FNN model from image words, but could estimate within 10% errors. Lastly, in order to improve accuracy of the estimation, a FNN model for estimation of the total evaluation from both analytical data and image words was constructed. This model showed the highest accuracy among all models. "<I>Jyouhinna-gehinna", "<I>shareta-yabona" among image words and "average value of green color", "arithmetical mean deviation", "peak count" among analytical data were needed to construct the model for estimation of the total evaluation.

U2 - 10.1252/kakoronbunshu.24.18

DO - 10.1252/kakoronbunshu.24.18

M3 - 記事

VL - 24

SP - 18

EP - 23

JO - Kagaku Kogaku Ronbunshu

JF - Kagaku Kogaku Ronbunshu

SN - 0386-216X

IS - 1

ER -