在產品設計領域裡，了解消費者於產品造型特徵的情感反應對於發展成功的產品非常有助益。然而，消費者需求通常都不盡相同而且相異，多數的研究都忽略了消費者偏好模式皆相異的性質，以致於建構出來的預測模型在實際生活中的應用價值並不高。本研究提出一感性工學架構，藉由消費者分群和資訊融合的概念，統整消費者對於產品的情感反應模型。首先，以模糊聚類分群（Fuzzy c-means，FCM）的技術分割消費者，利用消費者的偏好模式將同質性的消費者集結成一個群組。在每一個消費者的群組裡，可以根據模糊聚類分群的結果，獲得每位消費者的相對重要性和成對消費者之間的互動關係。接著使用「支援向量回歸（Support Vector Regression，SVR）」建構每位消費者的個人化感性預測模型（individual affective response prediction model，IARPM），經由支援向量回歸本身優越的歸納能力，消費者的個人化感性預測模型都有很好的表現。最後使用一個模糊積分運算子，稱為二次累加choquet 積分（2-additive Choquet integral），考慮了消費者在群組中的相對重要性以及消費者之間互動關係的情況下，整合所有消費者的個人化感性預測模型成為一個一致性感性預測模型（consensus affective response prediction model，CARPM）。透過這個感性工學架構可以準確的預測消費者情感反應，並且以視覺化的方法觀察分群情形再適當的將消費者分群，此外，以資訊融合技術更合理的整合目標族群的消費者情感反應。本研究以手機為案例展示所提出的感性工學架構，結果顯示以資訊融合概念的混合式專家架構對於處理消費者情感反應是一種很有潛力的方法。

Within the industrial design field, prediction models, which can analyze the relationship between consumers’ affective responses (CARs) and product form features (PFFs), have been studied extensively since CARs toward the product represents a mode of human-product interaction. However, despite a vast amount of literatures available on the subject, the heterogeneous nature of consumer preference patterns is often neglected so that the resulting prediction model is of less value for the real-word applications. This paper proposes a Kansei engineering framework for constructing the unified consensus affective response prediction model (CARPM) for CARs based on the concepts of consumer segmentation and information fusion. First, a fuzzy c-means (FCM) clustering is applied to separate the consumers with heterogeneous preference patterns into homogenous groups. In the target group, the relative importance of each consumer and the interaction between pairs of consumers can be determined according to the results of FCM clustering. Then, a state-of-the-art machine learning approach known as “support vector regression (SVR)” is used to construct the individual affective response prediction model (IARPM) for each consumer. These IARPMs have outperforming predictive ability of the affective responses due to the good generalization performance of the SVR algorithm. Finally, a fuzzy integral aggregation operator, namely the 2-additive Choquet integral, is employed to conjoint the IARPMs in each group by considering the relative importance and interactions of consumers in the target group to build the CARPM. According to the proposed framework, CARs toward PFFs can be predicted precisely by IAPRMs, and consumer groups can be clustered meaningfully by visualized way. Furthermore, CARs of the target groups could be gathered by considering the inherent interaction among the consumers. A case study of mobile phone is used to demonstrate the proposed approach. The results show that the proposed methodology is, an information fusion concept for handling the consumers’ evaluations of the target group, providing that mixture of experts is an alternative for dealing with CARs for product differentiation.

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