[Retracted] Decision‐Making Model of Product Modeling Big Data Design Scheme Based on Neural Network Optimized by Genetic Algorithm

Ming, Hu

doi:10.1155/2021/9315700

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…In [29], the application of deep NNs in DM problems is analyzed, and the better efficiency of NN-based DM systems is shown. In [30], the genetic algorithm is suggested to develop an NN-based DM. In [31] a fuzzy NN is developed for a multi-objective problem.…”

Section: Introductionmentioning

confidence: 99%

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

Hong

Zhao

Kausar

et al. 2022

Computational Intelligence and Neuroscience

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This paper presents a new approach to solve multi-objective decision-making (DM) problems based on neural networks (NN). The utility evaluation function is estimated using the proposed group method of data handling (GMDH) NN. A series of training data is obtained based on a limited number of initial solutions to train the NN. The NN parameters are adjusted based on the error propagation training method and unscented Kalman filter (UKF). The designed DM is used in solving the practical problem, showing that the proposed method is very effective and gives favorable results, under limited fuzzy data. Also, the results of the proposed method are compared with some similar methods.

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Section: Introductionmentioning

confidence: 99%

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

Hong

Zhao

Kausar

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…Cong, Chen, and Zheng [16] and Zhang, Su, and Liu [17] used entropy theory to weight the emotional imagery space of users, designers, and engineers, and constructed a composite imagery space evaluation model to establish the correspondence between emotional imagery and product modeling and to guide the modeling design of new products. Xue [18] and Hu [19] coupled the imagery and shape of the product to optimize the design and established an optimization model by genetic algorithm to obtain a refined form that meets the emotional imagery of consumers, which can generate a product form that better meets consumer expectations. Dong et al [20] and Wang, Li, and Wang [21] used the principle of extension design to model the quantitative relationship between product image and modeling features and realized the idea of quickly matching the optimal modeling design scheme according to user image needs.…”

Section: Introductionmentioning

confidence: 99%

Research on the Intelligent Modeling Design of a Truck Front Face Driven by User Imagery

Li,

Zheng,

Wang

et al. 2023

Applied Sciences

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The design of the front face of a truck can directly affect the user’s sensory evaluation of the vehicle. Therefore, based on Kansei Engineering theory and deep learning technology, this paper proposes an intelligent design method for the rapid generation of truck front face modeling solutions driven by user images. First, through Kansei Engineering’s relevant experimental methods and scientific data analysis process, the emotional image of the truck’s front face is deeply excavated and positioned, and the corresponding relationship between the characteristics of the truck’s front face and the user’s emotional image cognition is explored. Then, we used the generative confrontation network to integrate the user’s emotional image of the front face of the truck into the intelligent and rapid generation process of the new design scheme of the front face of the truck. Finally, the physiological data of the Electroencephalogram (EEG) experiment are used to evaluate the degree of objective matching between the generated modeling design scheme and the expected image. The purpose of this research is to improve the efficiency, reliability, and intelligence level of truck front face design, and to achieve a more personalized, precise, and high-quality design. This helps to improve the conformity of the modeling design scheme under specific image semantics.

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“…Hu analyzed the factors affecting product design decisions based on the development of products. This research introduced the basic process of product modeling, designed a model based on image processing under the background of big data, and built a decision-making model for product modeling and designed scheme under the big data cloud environment [ 42 ]. Finally, experiments proved that the decision-making model can improve the overall design efficiency, shorten the manufacturing cycle, and provide new ideas for product modeling design.…”

Section: Introductionmentioning

confidence: 99%

Multilayer Game Collaborative Optimization Based on Elman Neural Network System Diagnosis in Shared Manufacturing Mode

Tang

2022

Computational Intelligence and Neuroscience

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With the dynamic sharing of global resources shifted from the independent production of a single enterprise to and the development of collaborative manufacturing, today’s manufacturing industry has collaborative production among multiple enterprises. This paper extends the optimization method of the multienterprise dynamic equipment collaborative scheduling system with the dynamic manufacturing network theory approach, combining the neural network system and model predictive control theory to continuously and dynamically diagnose the changes of demand and capacity in the actual production process and design the corresponding multilayer game collaborative optimization robust model according to the uncertainty relationship. The study considers the production characteristics and management needs of multiple enterprises within the manufacturing industry, as well as the optimization of the dynamic equipment collaborative scheduling system, uncertain demand, dynamic equipment matching effectiveness, and equipment failure under the shared manufacturing model, deduces the market capacity demand for manufacturing enterprises by inverse prediction of dynamic market demand through the simple recurrent neural network (Elman network), and then maximizes the idle equipment utilization and load capacity under the shared manufacturing model in model rolling optimization. This paper gives model solutions based on big data analysis and combines improved population intelligence algorithms to determine the dynamic matching of manufacturing resources, product supply and demand paths, and transportation flows. The simulation results show that the application of model predictive control theory to the model of Elman neural network system diagnosis can effectively reduce the adverse effects of the two-way uncertainty environment on the profitability of enterprises. Meanwhile, considering the maximum performance matching of dynamic equipment scheduling and combining with robust optimization methods will reduce the cost loss of manufacturing enterprises in the worst case, and the cost loss reduction can reach 24.73%. The analysis results verify that the method is of great practical significance in the direction of intelligent production collaboration and industrial digital transformation.

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[Retracted] Decision‐Making Model of Product Modeling Big Data Design Scheme Based on Neural Network Optimized by Genetic Algorithm

Cited by 7 publications

References 10 publications

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

Research on the Intelligent Modeling Design of a Truck Front Face Driven by User Imagery

Multilayer Game Collaborative Optimization Based on Elman Neural Network System Diagnosis in Shared Manufacturing Mode

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