本研究提供一種簡單、有效、快速、又經濟的實驗設計分析法。本研究以田口方法為基礎，結合灰關聯分析與模糊控制器設計發展智慧型多品質適化之特性系統。此系統利用灰關聯分析，它俱有處理模型不確定與少數數據的特性，與模糊控制之推理解決各品質特性之相對重要性。

本實驗應用模糊控制器推論得到較客觀的綜合性多品質特性指標(MPCI) ，取代工程師的主觀經驗判斷，而以相對品質之重要性作為最佳組合，確保實驗準確度。實驗結果經由變異數分析，可確認溫度控制器之參數如加溫棒數量，濾器裝置位置，加溫時間與水混濁度為小型水族箱放養環境之顯著控制因子，他們對整體變異之貢獻度大約佔62%，最適水準確認實驗之灰關聯多重品質特性指標經模糊控制器推論結果相當接近於1'而個別品質特性溫度，比重，PH值分別接近預定目標值，且與各品質目標值誤差百分比均低於2%。結果顯示所提出以田口方法為基礎，結合灰關聯分析與模糊控制器設計方法，經適化後可有效達到滿意的結果。

The optimization of intelligent multiple performance characteristics can be accomplished by using the Taguchi-based method along with grey relational analysis and a fuzzy logic controller. The grey relational analysis solves the problems for model-uncertainty and data scarceness， while the fuzzy logic controller takes into account the relative importance of each individual quality characteristic. This paper proposes a simple and effective way of developing an efficient and systematic design.

In this study， we apply the proposed procedure to optimize the environment of a small-scale aquarium. Taking the relative importance of the multiple-performance characteristics into consideration， we employ a fuzzy logic controller to generate multiple-performance characteristics indices (MPCI) as an indicator of the overall quality characteristics. From the experimental results， the most significant control factors can be identified as: the water filter set-up， the heating time， the quantity of quartz heaters and the turbidity of the water. These factors account for 62% of the total variance. The confirmation run，at the optimal setting， was conducted and shows that all the individual quality characteristics reach their desired target values with errors well below 2%， and their overall multiple-performance characteristics were achieved satisfactorily.