【摘要】：消費(fèi)者重視產(chǎn)品的情感滿足,綜合運(yùn)用感性工學(xué)、心理學(xué)、設(shè)計(jì)學(xué)和智能信息等技術(shù)設(shè)計(jì)蘊(yùn)含消費(fèi)者潛在情感需求和喜好的產(chǎn)品顯得尤為重要。 本文以分析產(chǎn)品意象造型進(jìn)化設(shè)計(jì)的整體流程出發(fā),提出了確定多目標(biāo)意象、確定研究樣本及參數(shù)化樣本、建立產(chǎn)品意象造型評(píng)價(jià)系統(tǒng)和建立產(chǎn)品意象造型進(jìn)化設(shè)計(jì)系統(tǒng)四個(gè)階段的整體流程,并以其為研究主線。第一階段中,研究了確定多目標(biāo)意象的聚類分析法、主成分分析法和多元尺度法等技術(shù)方法,并以汽車為例利用SPSS軟件通過K.Means聚類分析確定了汽車的“豪華”、“力量”、“穩(wěn)重”、“親和”、“可愛”和“動(dòng)感”六個(gè)目標(biāo)意象。第二階段中,研究了參數(shù)化樣本的曲線控制法、參數(shù)模型法和頻譜分析法等技術(shù)方法。提出了汽車造型的表達(dá)、分解及參數(shù)化。通過曲線控制法定位主造型線的關(guān)鍵控制點(diǎn)坐標(biāo)值,以此作為研究樣本的參數(shù)。第三階段中,研究了建立產(chǎn)品意象造型評(píng)價(jià)系統(tǒng)的模糊聚類分析、數(shù)量化一類和人工神經(jīng)網(wǎng)絡(luò)等技術(shù)方法,其中人工神經(jīng)網(wǎng)絡(luò)包括BP神經(jīng)網(wǎng)絡(luò)、徑向基神經(jīng)網(wǎng)絡(luò)和模糊神經(jīng)網(wǎng)絡(luò)。提出了基于模糊神經(jīng)網(wǎng)絡(luò)的評(píng)價(jià)系統(tǒng)輸入層、神經(jīng)元層和輸出層的構(gòu)建,并建立了汽車意象造型評(píng)價(jià)系統(tǒng)。第四階段中,研究了產(chǎn)品單目標(biāo)意象造型進(jìn)化設(shè)計(jì)的常用技術(shù)方法,其歸納為遺傳算法、群智能算法、交互式進(jìn)化算法和混合算法四種類型的進(jìn)化算法。提出了產(chǎn)品多意象造型進(jìn)化設(shè)計(jì)的模型和基于NSGA-Ⅱ算法的產(chǎn)品多意象造型進(jìn)化設(shè)計(jì)技術(shù)。利用Matlab開發(fā)了基于NSGA-Ⅱ算法的汽車多意象造型進(jìn)化設(shè)計(jì)交互系統(tǒng)。對(duì)進(jìn)化結(jié)果進(jìn)行調(diào)查分析,表明本文提出的整體流程是可行的,提出的產(chǎn)品多意象造型進(jìn)化設(shè)計(jì)的模型和基于NSGA-Ⅱ算法的產(chǎn)品多意象造型設(shè)計(jì)的應(yīng)用是有效的。
[Abstract]:Consumers attach great importance to the emotional satisfaction of products, and it is particularly important to design products that contain consumers' potential emotional needs and preferences by using such technologies as perceptual engineering, psychology, design and intelligent information. Based on the analysis of the whole process of evolutionary design of product image modeling, this paper proposes to determine the multi-objective image, determine the research sample and parameterized sample. The whole process of the four stages of product image modeling evaluation system and product image modeling evolutionary design system is established, and the main line of study is the product image modeling evaluation system. In the first stage, the cluster analysis method, principal component analysis method and multivariate scale method are studied to determine the multi-objective image. Taking the automobile as an example, the "luxury" and "strength" of the automobile are determined by K.Means clustering analysis using SPSS software. "steady", "affinity", "lovable" and "moving" six target images. In the second stage, the curve control method, parametric model method and spectrum analysis method are studied. The expression, decomposition and parameterization of automobile modeling are proposed. The coordinate value of the key control point of the main modeling line is located by the curve control method, which is used as the parameter of the research sample. In the third stage, the fuzzy clustering analysis, quantification and artificial neural network are studied to establish the product image modeling evaluation system. The artificial neural network includes BP neural network. Radial basis function neural network and fuzzy neural network. In this paper, the input layer, neuron layer and output layer of evaluation system based on fuzzy neural network are proposed, and the evaluation system of automobile image modeling is established. In the fourth stage, the common techniques of product single-objective image modeling evolutionary design are studied, which can be divided into four types: genetic algorithm, swarm intelligence algorithm, interactive evolutionary algorithm and hybrid algorithm. The evolutionary design model of product multi-image modeling and the evolutionary design technology of product multi-image modeling based on NSGA- 鈪，

本文編號(hào)：2252474