BP神经网络在产品配色中的应用研究

目的实现产品配色设计的自动化。方法以感性工学为理论基础,采用神经网络为关键技术来研究产品配色。首先确定产品色彩的感性词汇与色彩设计要素,然后利用BP神经网络模型建立色彩编码与感性意象评价值之间的关系,最后结合豆浆机实例,设置BP模型中的输入层、输出层、隐含层的相关参数来进行豆浆机配色感性意象设计的实验仿真。结果测试验证了BP神经网络在产品配色设计中应用的有效性。结论表明了产品感性意象与产品配色之间的关系,论证了利用BP神经网络建立产品配色辅助设计系统的可行性。     

基于BP神经网络的轿车造型意象设计与量化预测模型建立

目的 构建产品造型意象设计与产品造型量化间的预测模型,将产品的设计元素与消费者的需求和感知相关联。方法 分析小轿车造型与感性意象之间的关系,基于用户对产品外观形态感受进行打分,采用感性工学方法进行量化;用双BP神经网络训练的思路,建立外观设计元素与感性意象间的对应关系,得到小轿车外观方案的感性意象预测模型。结论 获得产品造型设计方案的感性意象预测模型,能高效解决设计师的产品造型设计方案与消费者的目标达成趋同,通过高效的产品外观设计,为未来轿车及机电类产品的造型设计转向以消费者精神需求为导向提供了一种新的设计方法。     

BP神经网络的宇航服多维意象造型评价方法

目标 为适应宇航服造型设计对“大国形象”多维意象识别的需要,提出了以“大国形象”为导向的宇航服造型设计评价模型构建方法。方法 在大量用户调研的基础上,对“大国形象”意象集进行筛选,并采用认知实验与聚类分析相结合的方法获得宇航服产品的代表样品。采用语义差异法,对“大国形象”感性意象集中初步筛选词和宇航服产品代表样本之间的映射数据进行检验和分析,对得到的数据结果进行主成分分析,以获得宇航服产品对“大国形象”感性意象的认知空间。同时,根据全局HIEs解构原则和宇航服产品的功能约束清单建构宇航服产品造型特征空间。采用语义差异法和认知测试,得到宇航服造型多维意象认知空间。利用BP神经网络,以样本关键HIEs评价经数字编码后作为输入层,以各意象词下样本的感性意象均值作为输出层,构建宇航服造型意象评价模型。随后,采用留一交叉训练法对评价模型的准确性进行验证。结论 该评价模型能够有效解决造型特征与多维意象之间的映射及匹配,论证了造型意象和认知空间之间存在的关联性,探索出设计目标和设计意象关联判断的实践方法。     

产品感性意象设计研究进展

目的 产品感性意象设计是将用户情感转化为产品设计要素的设计模式,在提高用户满意度方面具有重要作用,基于此,对产品感性意象设计的研究进展进行梳理和分析.方法 通过对国内外相关文献的综述,论述了感性意象的有关概念;重点分析了产品感性意象设计所涉及的代表性样本确定、产品设计要素分析、感性意象获取、感性意象与产品设计要素的映射构建及产品感性意象优化设计等研究方法和关键技术,并归纳总结了各类研究方法和关键技术的优缺点;在感性意象应用方面,着重分析了感性意象中针对产品造型、产品色彩及产品材质的研究方向.结论 随着感性意象和新技术、新方法的融合发展,更加准确地获取感性意象、提高感性意象映射模型的精度、智能化设计、设计过程虚拟化等将是未来产品感性意象设计研究的热点.     

基于感性意象的产品造型优化设计

针对当前消费者感性需求的提高,运用组合思想提出基于感性意象的产品造型优化设计方法.首先应用感性工学类目层次法得到产品的设计要素,再应用数量化一类理论建立感性意象和造型特征元素之间的对应关系,作为优化产品造型时的评判依据.然后应用蚁群算法对造型特征元素进行组合优化,实现了以少量的产品属性形态构建多样的产品造型方案,从而快速设计出满足消费者感性意象需求的产品.最后以电热水壶设计进行实例研究,结果表明运用该方法可有效地进行产品意象造型优化设计.     

基于KAB模型的设计要素组合推导

目的 为了提高产品感性设计开发效率及意象匹配精度,采用定性和定量相结合的方法,提出一种基于层次分析法(AHP)与BP神经网络相结合的产品意象设计要素组合推导方法。方法 首先通过网络爬虫和亲和图法建立产品意象及造型数据库,以获得意象和设计要素;其次运用AHP构建产品层次结构模型及判断矩阵,计算意象及设计要素的权重系数;接着,基于形态拆解法与权重结果获得设计要素类型及优化组合编码,再运用语义差异法(SD)获取组合编码的用户感性意象均值;最后通过感性工学和AHP-BP神经网络构建KAB关键设计要素组合预测模型。结果 基于此模型预测四旋翼无人机设计方案,应用逼近理想解排序法(TOPSIS)对其进行验证评价,结果表明通过模型计算能够得到与目标感性意象高度匹配的设计要素组合编码。结论 基于此模型能够快速获得客观准确的产品意象造型设计要素组合,提高产品设计开发过程的效率。     

基于感性工学的儿童陪伴机器人造型设计

目的 为了满足儿童群体对家庭陪伴型机器人的审美及情感需求,更好地感知体验造型形象,提高儿童陪伴机器人造型的创新性和差异性,让父母和孩子更加青睐.方法 运用感性工学理论,构建了儿童陪伴机器人意象造型与感性认知映射模型,采用语义差异法和Likert量表对代表性样本进行感性意象认知分析,通过SPSS软件统计感性意象词汇测量数据,进行主成分分析和因子系数数量化分析,语义量化分析得出主要造型特征影响元素,依据主要的影响元素对儿童陪伴机器人进行造型设计.结论 明确意象造型与感性认知的映射关系以及感性需求相关性分析的方法,将无形的感性意象词汇转化为有形的造型设计元素,可以有效指导儿童陪伴机器人进行造型创新设计,为该行业的产品造型研究方法提供参考.     

基于结构方程模型的手机产品意象造型设计

提出基于结构方程模型的产品意象造型优化设计方法.选择目前市场流行而又缺少感性设计研究的滑盖手机为研究对象,运用语意差异法、因子分析法提取内隐的用户意象语意信息,运用形态分析法分析产品造型特征,确定产品造型要素,再运用结构方程模型求取顾客偏好模型以及造型要素与意象词对之间的量化关系模型,并进行了模型拟合.模型清晰地反映了顾客偏好、造型要素和意象词对之间的量化关系,为产品造型优化设计提供了思路和方法.     

基于感性工学的老年陪护机器人造型设计

目的以感性工学相关理论为支撑,研究老年陪护机器人的造型设计。方法通过语义差异法分析市场现有产品,结合Likert心理量表构筑产品感性意象评价体系。根据意象尺度法,从造型、色彩、材质3个因素绘制意象尺度图,再通过层次分析法进行推论。通过意象尺度图将现有产品风格约束下的老年人用户群体的感性意象转化为设计要素,应用于老年陪护机器人的造型设计。结论感性工学可以有效应用于情感陪护类产品的造型设计。     

基于感性意象的白酒酒瓶造型多维度特征分析

目的 为了更深层地探寻消费者对白酒酒瓶造型设计的情感诉求,找到影响消费者感性意象的设计要素并构建情感意象与造型要素之间的联系.方法 提出一种从多维设计变量出发对白酒酒瓶造型进行感性工学研究的方法,从消费者的感性意象出发,运用人机界面元件(HIEs)解构方法对白酒酒瓶造型进行多维度设计特征分析,然后通过灰关联与因子分析筛选出最具有代表性的设计特征元素,并运用数量化Ⅰ类建立白酒酒瓶主设计特征元素与消费者感性意象之间的映射关系模型.结果 设计出新产品对构建的白酒酒瓶感性意象预测模型进行实例验证分析,模型预测值与实际评价值的T检验结果证明了该模型的有效性和可靠性.结论 实现了对白酒酒瓶造型的意象评价值预测,并了解到目标感性意象对应的设计特征要素.     

步行器精密测力系统中的人工神经网络标定新技术

设计了一种可用于步行器精密测力系统标定的3层反向传播人工神经网络模型,以测力系统12导联应变片电桥输出电压作为网络输入向量,6个负载分量力作为网络输出向量,并通过目标误差下的绝对误差和对比确定出模型隐含层的最优神经元数.有关误差校验结果表明,使用该神经网络技术标定后的系统最大单向力精度误差为7.78%,最大交叉干扰为7.49%,与传统的线性标定方法相比,能够有效提高步行器受载力的测量精度并大大降低干扰误差,未来有望为步行器助行康复训练效果的准确监控和评估提供帮助.     

基于GNSA多尺度模型的人脸识别

考虑近似图像信息和细节图像信息,提出了 GNSA 多尺度模型。该模型利用具有 1 个隐含层和 50 个隐单元的神经网络建立不同尺度图像间的映射关系;使用反向传播算法训练神经网络,确定这种映射关系;根据该映射关系由低分辨力图像估计高分辨力图像。采用亮度相似性和对比度相似性量化估计图像与目标图像间的相似程度。实验表明,以该模型分析得到的两种相似性分别为 89.907%和 96.196%;以该模型为基础的人脸识别系统对光照具有很好的鲁棒性。     

A neural network-based algorithm that searches for the measuring points of a rule surface

The purpose of this study is to use a proposed neural network-based algorithm to explore the determination of the recommended measuring points for a rule surface. The task of measuring a rule surface starts from the rule surface design blueprint. Mesh grid data on the designed rule surface were selected. The pattern recognition capability of the back-propagation neural network is explored in this article. The network learning was successfully performed by the learning and testing of the network, the support of a designated acceptable perpendicular error value, a learning model in which training examples were gradually added and the adjustment of the number of training examples according to the network structure.     

Neuro-Knowledge-Based Expert System (NKBES) for Optimal Scheming of Die Casting Process

We develop a neuro-knowledge-based expert system (NKBES) frame in this work. The system mainly concerns with decision of gating system and die casting machine based on a neuro-inference engine launched under the MATLAB software environment. For enhancement of reasoning agility, an error back-propagation neural network was applied. A rapidly convergent adaptive learning rate (ALR) and a momentum-based error back-propagation algorithm was used to conduct neuro-reasoning. The working effect of the system was compared to a conventional expert system that is based on a two-way (forward and backward) chaining inference mechanism. As the reference, the present paper provided the neural networks sum-squared error (S5E) and ALR vs iterative epoch curves of process planning case mentioned above. The study suggests that the neuro-modeling optimization application to die casting process design has good feasibility, and based on that a novel and effective intelligent expert system can be launched at low cost.     

Modeling surface tension of pure refrigerants using feed-forward back-propagation neural networks

In this study, a model was proposed to predict the surface tension on the basis of feed-forward back-propagation network by employing different training algorithms including Levenberg–Marquardt, Scaled Conjugate Gradient and Pola–Ribiere Conjugate Gradient. A total of 793 experimental data points from 24 different pure refrigerants were gathered from reliable literature to train, test and validate the proposed network. Temperature, critical pressure, critical temperature, and acentric factor were chosen as input variables of the developed network. The network with 1 hidden layer and 19 neurons with tan-sigmoid and purelin transfer functions in the hidden and output layers was determined to have the optimum performance. The results revealed that the proposed network has the ability to correlate and predict the surface tension accurately with an overall Mean Relative Error (MRE) value of 0.0074 and correlation coefficient (R²) of 0.9996. The obtained results were compared to different well-known correlations in the literature which demonstrated a better performance of the proposed network.     

隧道爆破振动的BP神经网络预测及应用研究

以福厦高速公路扩建工程中的大帽山小净距大跨度隧道爆破开挖为背景,应用BP神经网络,建立了小净距隧道掘进爆破振动和速度峰值的预报模型,并成功地应用于该隧道进、出口段掘进爆破振动控制工程实践.分析比较结果表明,BP神经网络预报方法的预测结果与实测值的平均误差为7.16%～10.61%,较传统预报方法的预测精度提高20%以上.研究成果为小净距大跨度隧道爆破振动控制提供了新的预测方法,具有理论及实用参考价值.     

基于BP神经网络的真空度测量精度改善方法研究

针对真空度测量精度低的现状,提出一种真空度测量精度改善方法。以热偶规为研究对象,基于BP神经网络设计真空度测量系统。通过设计BP网络结构,采用三种不同的算法对网络权值进行训练,以获得尽可能稳定、精度更高的BP神经网络。对三种算法由测试样本进行测试,三种算法训练的网络能较大程度地提高真空度测量精度,受热丝电流、热丝冷阻干扰影响大大减小;而且最速下降法对应的网络输出值波动较大,附加动量法次之,自适应学习速率调整法对应的输出值波动最小,波动量小于0.01。     

A Study on Turbo—rotor Multi—fault Diagnosis Based on a Neural Network

The multi-fault phenomena are common in the turbo-rotor system of a liquid rocket engine.As it has many excellent qualities,the neural network might be used to solve the problems of multi-fault diagnasis of a turbo-rotor system.First,the feature expression of a common turbo-rotor fault was studied in order to build up the standard fault pattern and satisfy the need of neural network studying and diagnosing.Then.the turbo-rotor fault identification and diagnosis problems were investigated by using a BP(back-propaga-tion)neural network.According to the BP neural network problems,the parallel BP neural network method of multi-fault diagnosis and classification was presented and investigated.The results indicated that the parallel Bp neural network method could solve the turbo-rotor multi-fault diagnosis problems.     

Neural network recognition of chemical class information in mobility spectra obtained at high temperatures

A minimal neural network was applied to a large library of high-temperature mobility spectra drawn from 16 chemical classes including 154 substances with 2000 spectra at various concentrations. A genetic algorithm was used to create a representative subset of points from the mobility spectrum as input to a cascade-type back-propagation network. This network demonstrated that significant information specific to chemical class was located in the spectral region near the reactant ions. This network failed to generalize the solution to unfamiliar compounds necessitating the use of complete spectra in network processing. An extended back-propagation network classified unfamiliar chemicals by functional group with a mean for average values of 0.83 without sulfides and 0.79 with sulfides. Further experiments confirmed that chemical class information was resident in the spectral region near the reactant ions. Deconvolution of spectra demonstrated the presence of ions, merged with the reactant ion peaks that originated from introduced samples. The ability of the neural network to generalize the solution to unfamiliar compounds suggests that these ions are distinct and class specific.     

Optical finite impulse response neural networks

The finite impulse response neural network is described in detail. Different algorithms capable of temporal back-propagation are considered, including a novel modification to the conventional algorithm, called the delayed-feedback back-propagation algorithm. We present and analyze different optoelectronic processors making use of adaptive volume holograms and three-dimensional optical processing. Two single-layer architectures are presented: the input delay plane architecture and the output delay plane architecture. By combining them it is possible to implement both forward and backward propagation in two multi-layer architectures: the first making use of the conventional temporal back-propagation and the second making use of delayed-feedback back-propagation.