Dynamic Multi-Response Experiments by Backpropagation Networks and Desirability Functions

以倒傳遞網路及望想函數分析動態多品質特性實驗

10.29977/JCIIE.200607.0002

張旭華(Hsu-Hwa Chang)

倒傳遞網路 ； 望想函數 ； 動態多品質 ； 參數設計 ； 績效衡量 ； backpropagation network ； desirability function ； dynamic multi-response ； parameter design ； performance measurement

工業工程學刊

23卷4期（2006 / 07 / 01）

280 - 288

英文

目前雖然有很多成熟的技巧分析參數設計，但處理處理動態多品質問題的方法則較缺乏。本研究利用倒傳遞網路及望想函數提出一個新的方法以分析動態多品質特性問題；藉由所提出的一個新的績效衡量指標以評估不同型態的動態反應值，並轉換為單一指標。所提出的方法乃利用倒傳遞網路訓練實驗資料以建立動態多品質特性的反應模型，該反應模型能預測出所有參數組合的反應值；透過績效評估可以求得最佳參數組合。本方法藉由分析一組實驗數據展示其有效性。

Although there are some skillful techniques to analyze the parameter design problems, the methods for tackling the dynamic multi-response are rare. This work proposes an approach based on backpropagation neural networks and desirability functions to optimizing parameter design of the dynamic multi-response. A novel performance measurement of dynamic multi-response is developed to apply the desirability function that integrates several different types of dynamic responses into a single index. The proposed approach employs a BPN to construct the response model of the dynamic multi-response system by training the experimental data. The response model is then used to predict all possible multi-responses of the system by presenting full parameter combinations. Through evaluating the performance measurement of the predicted dynamic multi-response, the best parameter setting can be obtained by maximizing the single index. An illustrative example is analyzed to demonstrate the effectiveness of the proposed approach.

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