摘要
　　完整之生命週期評估需耗費大量時間、物力與財力，現有之生命週期評估方法，大都在產品已完成設計階段，量產上市之後，對其做完整之生命週期評估，再發現其對環境之衝擊之大小，以此結果來評斷產品可修正之方向。但由於產品已於量產階段，所可修改之幅度不大，但如果能於產品尚於設計階段便能得知其對環境影響之多寡，便能於產品尚未進入生產階段，來進行修正，能夠做大幅度的調整。

　　雖然已有些簡化生命週期評估方法被提出於概念設計階段使用，但這些方法存在著環境衝擊評估值不夠準確、所需要之資料過於龐大、所用之方法操作過於複雜等問題。

　　本文所提出之改良方法為利用類神經網路，針對單一產品與單一生命週期環境衝擊評估值，來進行類神經網路架構之訓練，利用現有產品資料，選定我們要針對之特定環境衝擊評估項目，根據其產品性質去選取類神經網路訓練特徵，再規範特徵範圍對應類神經訓練代碼，類神經網路訓練完成經驗證其誤差後，確認其準確度，便完成類神經網路的架構。再來對於產品生命週期環境衝擊評估資料過少者，無法用於上述方法，提供現有以模式為基礎之定性評估資訊案例庫，針對此產品位於生命週期各階段對環境之影響多寡，提供資訊以供參考，用於概念設計階段，設計新產品時改善對環境之衝擊參考之依據。

　　此方法能夠有效的提高環境衝擊評估值的正確性，亦能降低所需資料的數量，於方法架構方面也能降低其複雜性，在實際應用方面亦能擺脫產品於概念設計階段，各項數據資料之不確定，無法進行確切評估之問題。對於數據資料過少之產品，無法使用類神經網路來訓練，亦能提供針對產品各生命週期階段之環境衝擊參考數據。

Abstract

　　Traditional Life-cycle assessment(LCA) methods need lots of times and resources for numerical calculation. In case we can find the environmental impacts of products on the stage of conceptual design, a large range of products’ specifications could be changed to improve the eco-properties of products. Some simple LCA methods are developed to be executed on the early design stage. However, there are some defects such as needing lots of data, lacking for precise estimated value and the complex operating processes etc. An improved method is proposed to reform the shortcomings that deal with only single product and single impact category. By using the artificial neural networks to train the existent LCA data, one can calculate the environmental impacts of products at the conceptual design phase. The new method proposes the idea of defining characteristic range instead of using precise number in input value of artificial neural networks to response the shortage of detail product information. As for the problem of few data of existent product, the designer can consult the database of pattern-based qualitative LCA to get the environmental impact map. According to the environmental impact map the design engineer can check which life stage of products will make the highest influence of environment. This method can increase the accuracy of impact results, reduce the operating difficulties, lessen the data requirement and overcome the problem of no detail product specification.

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