Product form evolutionary design system construction based on neural network model and multi-objective optimization

Wu, Yixiang

doi:10.3233/jifs-201439

Cited by 16 publications

(13 citation statements)

References 43 publications

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“…In this paper, the fractional calculus approach is used to generalize some ANN models proposed in industrial product form design. The proposed discrete fractional-order model (3) extends the existing integer-order models [5][6][7][8][9][10][11] to the fractional-order case. Compared to other existing works, the use of the fractional-order operator in (3) overcomes the limitations of using integer-order differences.…”

Section: The Proposed Fractional Discrete Calculus Neural Network Mod...mentioning

confidence: 99%

“…The authors in [8] proposed an ANN model to relate design parameters and produce a product form aesthetics design depending on customer perceptions in a more systematic and intelligent manner. The paper [9] considered a hybrid product form design method based on a specific class of ANNs from the perspective of multi-objective optimization. The paper [10] considers a number of ANN models, examining how a given product form affects the product image as perceived by customers.…”

Section: Introductionmentioning

confidence: 99%

“…(i) A new fractional nonlinear neural network modeling approach is applied to extend and generalize some existing ANN models applied in product form design analysis [5][6][7][8][9][10][11]. The proposed bio-inspired neural network approach is based on the use of discrete fractional calculus [62,63].…”

Section: Introductionmentioning

confidence: 99%

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Impulsive Control Discrete Fractional Neural Networks in Product Form Design: Practical Mittag-Leffler Stability Criteria

Stamov

2024

Applied Sciences

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The planning, regulation and effectiveness of the product design process depend on various characteristics. Recently, bio-inspired collective intelligence approaches have been applied in this process in order to create more appealing product forms and optimize the design process. In fact, the use of neural network models in product form design analysis is a complex process, in which the type of network has to be determined, as well as the structure of the network layers and the neurons in them; the connection coefficients, inputs and outputs have to be explored; and the data have to be collected. In this paper, an impulsive discrete fractional neural network modeling approach is introduced for product design analysis. The proposed model extends and complements several existing integer-order neural network models to the generalized impulsive discrete fractional-order setting, which is a more flexible mechanism to study product form design. Since control and stability methods are fundamental in the construction and practical significance of a neural network model, appropriate impulsive controllers are designed, and practical Mittag-Leffler stability criteria are proposed. The Lyapunov function strategy is applied in providing the stability criteria and their efficiency is demonstrated via examples and a discussion. The established examples also illustrate the role of impulsive controllers in stabilizing the behavior of the neuronal states. The proposed modeling approach and the stability results are applicable to numerous industrial design tasks in which multi-agent systems are implemented.

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Section: The Proposed Fractional Discrete Calculus Neural Network Mod...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impulsive Control Discrete Fractional Neural Networks in Product Form Design: Practical Mittag-Leffler Stability Criteria

Stamov

2024

Applied Sciences

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“…At present, product Kansei image design is one of the research hotspot for industrial design. Experts and scholars have conducted related research in bicycle saddle design [8], automobile design [9][10][11][12][13][14], financial equipment design [15,16], sports shoes design [17], electric motorcycle design [18], and electric scooter design [19].…”

Section: Introductionmentioning

confidence: 99%

Target Mining and Recognition of Product Form Innovation Design Based on Image Word Similarity Model

Zhang

Liu

Zhang

et al. 2022

Advances in Mathematical Physics

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Product Kansei image design is one of the research hotspot of product emotional design. Due to the subjectivity, low efficiency, and low level of intelligence in the existing product form innovation design methods in the mining of design goals. This study combines the semantic dictionary of Tongyici Cilin with Kansei engineering theory and uses clustering analysis algorithm, semantic difference method, and word similarity calculation method to realize product Kansei image design. Tongyici Cilin is a computable Chinese semantic dictionary. In this study, we innovatively introduced Tongyici Cilin into the image word similarity calculation in product image design. First, the product image design process based on Tongyici Cilin is proposed. Then, we establish a model of image word similarity calculation using the common distance, difference distance, common adjustment parameter, and differential adjustment parameter. By comparing with international standard data, it is confirmed that the image word similarity calculation model proposed in this article is effective and efficient. Using the sedan image design of middle-aged, middle-income men as an example, the sedan form style of each target image was successfully derived from the Internet-based questionnaire. Based on the case studies, we determined that it is effective to use the Tongyici Cilin semantic dictionary to determine the target image and improve the efficiency of product image design.

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“…Aesthetic elements can be extracted from product forms with computational methods (Catalano et al , 2007). Given the fast-developing intelligent computing technologies (Wu, 2020; Cio et al , 2021; Longo et al , 2021), aesthetics quantification is increasingly important to enhance form design performances.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary form design method based on aesthetic dimension selection and NSGA-II

Wang

Zhu²,

Qi³

et al. 2022

AIEDAM

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In the era of rapid product update and intense competition, aesthetic design has been increasingly important in various fields, as aesthetic feelings of customers largely influence their purchase preferences. However, the quantification of aesthetic feeling is still a very subjective process due to vague evaluations. The determination of form parameters according to aesthetics is difficult hitherto. Aesthetic measure recently arises as a prominent tool for this purpose using formulas derived from aesthetic theory. But as revealed by existing studies, it needs to be customized with deterministic and objective methods to be reliable in practice use. To facilitate this application, this paper proposes an evolutionary form design method, integrating aesthetic dimension selection and parameter optimization. After summarizing initial aesthetic dimensions, aesthetic dimension selection based on expert decision-making and particle swarm optimization (PSO) is carried out. With filtered aesthetic dimensions, design parameters are optimized with NSGA-II (non-dominated sorting genetic algorithm). The quality of pareto solutions obtained to be design schemes is assessed by three criteria to conduct sensitivity analysis of cross and mutation probability and population size. Our experiment using bicycle form design shows that the proposed evolutionary form design method can generate numerous and variant aesthetic design schemes rapidly. This is very useful for both product redesign and innovative new product development.

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Product form evolutionary design system construction based on neural network model and multi-objective optimization

Cited by 16 publications

References 43 publications

Impulsive Control Discrete Fractional Neural Networks in Product Form Design: Practical Mittag-Leffler Stability Criteria

Impulsive Control Discrete Fractional Neural Networks in Product Form Design: Practical Mittag-Leffler Stability Criteria

Target Mining and Recognition of Product Form Innovation Design Based on Image Word Similarity Model

An evolutionary form design method based on aesthetic dimension selection and NSGA-II

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