A support vector regression based prediction model of affective responses for product form design

2010 ◽  
Vol 59 (4) ◽  
pp. 682-689 ◽  
Author(s):  
Chih-Chieh Yang ◽  
Meng-Dar Shieh
2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Meng-Dar Shieh ◽  
Yongfeng Li ◽  
Chih-Chieh Yang

Affective responses concern customers’ affective needs and have received increasing attention in consumer-focused research. To design a product that appeals to consumers, designers should consider multiple affective responses (MARs). Designing products capable of satisfying MARs falls into the category of multiobjective optimization (MOO). However, when exploring optimal product form design, most relevant studies have transformed multiple objectives into a single objective, which limits their usefulness to designers and consumers. To optimize product form design for MARs, this paper proposes an integrated model based on MOO and multicriteria decision-making (MCDM). First, design analysis is applied to identify design variables and MARs; quantification theory type I is then employed to build the relationship models between them; on the basis of these models, an MOO model for optimization of product form design is constructed. Next, we use nondominated sorting genetic algorithm-II (NSGA-II) as a multiobjective evolutionary algorithm (MOEA) to solve the MOO model and thereby derive Pareto optimal solutions. Finally, we adopt the fuzzy analytic hierarchy process (FAHP) to obtain the optimal design from the Pareto solutions. A case study of car form design is conducted to demonstrate the proposed approach. The results suggest that this approach is feasible and effective in obtaining optimal designs and can provide great insight for product form design.


2019 ◽  
Vol 27 (2) ◽  
pp. 126-143
Author(s):  
Yongfeng Li ◽  
Liping Zhu

Affective responses reflect consumers’ affective needs and have attracted considerable attention in industrial product form design. When designing a product for consumers, designers should take into account multiple affective responses. Therefore, designing products that can satisfy multiple affective responses is a multi-objective optimization problem. In this article, a novel model based on the robust posterior preference articulation approach is proposed to optimize product form design by simultaneously considering multiple affective responses. First, design analysis is performed to determine design variables and affective responses. Subsequently, the Taguchi method is used, and the signal-to-noise ratios are calculated. Based on the results, predictive models for signal-to-noise ratios concerning multiple affective responses are built and then a multi-objective optimization model is constructed. The reference-point-based many-objective non-dominated sorting genetic algorithm–II (called NSGA-III) is used to solve the multi-objective optimization model for obtaining Pareto solutions. Finally, a combination of the fuzzy Kano model and the fuzzy optimum selection model is adopted to select the optimal solution from the obtained Pareto solutions. A car profile design was employed to present the proposed approach. The results reveal that the proposed approach can effectively achieve an optimal design and is a robust approach for optimizing product form design.


2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Yongfeng Li ◽  
Meng-Dar Shieh ◽  
Chih-Chieh Yang ◽  
Liping Zhu

In today’s competitive market, industrial product form design is moving towards being consumer centric. Affective responses relate to customers’ affective needs and are receiving increasing attention. To design a product form that can appeal to consumers, designers should consider multiple affective responses (MARs). This paper proposes a robust design approach that uses a fuzzy-based hybrid Taguchi method to derive the optimal product form design concerning MARs. First, design analysis is used to identify design variables and MARs. According to the results, a Taguchi experiment is designed in which fuzzy sets are used to measure the MARs; then, signal-to-noise (S/N) ratios are calculated. Subsequently, a fuzzy questionnaire with multiple answers is employed to acquire consumers’ preference weights for MARs, following which Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is adopted to transform the multiple S/N ratios into a multiperformance characteristic index (MPCI). On the basis of the MPCI, the effects of design variables are identified through analysis of variance and the response table and the response graph are obtained. Consequently, the optimal form design is achieved. A car profile design was used as an example to demonstrate the proposed approach. The results indicate that this approach can effectively improve consumers’ affective response qualities and can be used as a robust design approach to optimize product form design.


2012 ◽  
Vol 23 (07) ◽  
pp. 1250055 ◽  
Author(s):  
J. L. TANG ◽  
C. Z. CAI ◽  
T. T. XIAO ◽  
S. J. HUANG

The purpose of this paper is to establish a direct methanol fuel cell (DMFC) prediction model by using the support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter selection. Two variables, cell temperature and cell current density were employed as input variables, cell voltage value of DMFC acted as output variable. Using leave-one-out cross-validation (LOOCV) test on 21 samples, the maximum absolute percentage error (APE) yields 5.66%, the mean absolute percentage error (MAPE) is only 0.93% and the correlation coefficient (R2) as high as 0.995. Compared with the result of artificial neural network (ANN) approach, it is shown that the modeling ability of SVR surpasses that of ANN. These suggest that SVR prediction model can be a good predictor to estimate the cell voltage for DMFC system.


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