本研究的目的在於建立一布料設計自動化的流程，運用類神經網路進行學習，再利用遺傳演算法將設計構想做有效的大量發散，將人類的感性思維帶入電腦程式運算中，使設計流程中之構想發展階段，藉由設計自動化達到更高的效率，刺激設計師靈感發散，提高布料設計構想發展的品質，降低設計的流程與成本。

　　以往設計師進行構想發展階段時，採用的是傳統手工繪製草圖，然而此一傳統方式在追求高效率高度競爭的經濟環境下，已然不適用。產品的生命週期明顯較以往縮短，加上電腦輔助設計軟體的技術進步，更縮短了產品設計時程。本研究著眼於設計流程中之構想發展階段，導入自動化設計概念，以期能獲得大量、快速且有用的設計構想，藉此激發設計師的創造力。

　　本研究方法是利用集群分析，將蒐集而得的女性衣服布料予以分群，挑選出代表性布料40張，將其以問卷對九組感性語彙對進行評分；抽取出布料的色彩黏著度當作其色彩特徵，並利用「極座標的傅立葉－小波描述子」（Polar Fourier-Wavelet descriptor）PFW之演算法抽取出布料的紋理特徵。以特徵值為輸入層，感性語彙得分為輸出層，進行倒傳遞類神經網路訓練。再將抽取出的布料特徵，運用遺傳演算法產生大量的特徵子代，並以學習成功的倒傳遞類神經網路為其評選標準，取得符合的特徵值，再依其特徵值產生新的布料。

The purpose of the research was to build a process of computer aided design for fabric. This paper calculated human percept with computer program, to learn with Neural Network Algorithm and to obtain a lot of concepts effectively with Genetic Algorithm. In the stage of conceptual design, it can increase efficiency and productivity by automatic design, stimulate designers’ imagination, improve the quality of fabric design, and reduce the cost of design process.

In the stage of conceptual design, designers are used to sketch by hand traditionally. However, the traditional way has not been suitable in this competitive economy. Because the cycle of productions is shortened and the technology of computer aided design improves obviously, the time for product design is reduced. This paper’s emphasis on the concept of automatic design is broached in the stage of conceptual design. It is looking forward to gaining a large number, fast and useful concept to encourage designers' creation.

At first, this paper uses Cluster Analysis. Female garments fabrics assembled were divided into groups and then we chose 40 representative fabrics from these groups. The subjects were invited to measure their subjective impression of 40 different fabric images using the Semantic Differential Method (SD). Second, we extracted Color Coherent Vector（CCV）as the color features of garments and the texture features of garments using Polar Fourier-Wavelet descriptor. The input layer is the features of garments and the output layer is the value of 9 impression words. The Back-Propagation Networks （BPN）was trained to approximate the relationship between kansei features and the features of garments. Third, we obtained a large number of filial generations by Genetic Algorithm, selected suitable filial generations by the successfully trained back-propagation network, and obtained new fabrics.

第一章 緒論
1-1 前言 …………………………………………………………… 1
1-2 研究動機 ………………………………………………………… 1
1-3 研究目的 ………………………………………………………… 3
1-4 研究範圍與限制 ………………………………………………… 4
1-5 研究架構 ………………………………………………………… 4
1-6 預期貢獻 ………………………………………………………… 6
1-7 論文內容說明 ……………………………………………………… 7

第二章 文獻探討
2-1 類神經網路 ……………………………………………………… 9
2-2 遺傳演算法 ……………………………………………………… 9
2-3 感性工學 ………………………………………………………… 10
2-4 色彩體系與人類視覺感知 …………………………………… 11
2-5 現有相關研究成果 ………………………………………… 14
2-5.1 影像搜尋 ……………………………………………… 14
2-5.2 色彩特徵 ……………………………………………… 15
2-5.3 紋理特徵 ……………………………………………… 19

第三章 研究理論架構
3-1 類神經網路 ……………………………………………………… 25
3-1.1 類神經網路架構 ………………………………………… 26
3-1.2 倒傳遞類神經網路架構 …………………………… 28
3-1.3 類神經網路的應用 ……………………………… 30
3-1.4 應用類神經網路於產品設計之探討 …………………… 31
3-2 遺傳演算法 ……………………………………………………… 32
3-2.1 遺傳演算法概述 ………………………………………… 32
3-2.2 遺傳演算法架構與流程 …………………………… 32
3-2.3 遺傳演算法的應用 ……………………………… 38
3-2.4 遺傳演算法於產品設計之探討 ……………………… 40

第四章 研究方法與步驟
4-1 研究步驟 ………………………………………………………… 42
4-2 樣本布料選定 ……………………………………………………… 44
4-3 感性語彙對選定 …………………………………………………… 48
4-4 問卷 …………………………………………………………… 49
4-5 布料圖片的特徵分析 ……………………………………………… 50
4-5.1 布料圖片色彩特徵的抽取 ………………………… 50
4-5.2 布料圖片紋理特徵的抽取 ………………………… 51
4-6 倒傳遞類神經網路訓練 ……………………………………… 53
4-6.1 色彩特徵與感性語彙對 …………………………… 55
4-6.2 紋理特徵與感性語彙對 …………………………… 60
4-6.3 色彩特徵及紋理特徵與感性語彙對 …………………… 63
4-7 產生新的色彩特徵 ………………………………………… 67

第五章 結論與未來展望
5-1 遺傳演算法應用探討 …………………………………………… 78
5-2 類神經網路應用探討 ……………………………………… 78
5-2.1 色彩特徵與感性語彙對 ……………………………… 78
5-2.2 紋理特徵與感性語彙對 …………………………… 79
5-2.3 色彩特徵及紋理特徵與感性語彙對 …………………… 79
5-3 程式內容探討 ……………………………………………………… 80
5-4 研究貢獻 ……………………………………………………… 80
5-5 未來展望 ………………………………………………………… 80

參考文獻…………………………………………………………………………………… 82

附錄………………………………………………………………………………………… 85

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