International Journal of Circuits, Systems and Signal Processing

E-ISSN: 1998-4464
Volume 15, 2021

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of NAUN Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

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Volume 15, 2021

Title of the Paper: A Consumer-Oriented Car Style Evaluation System Based on Fuzzy Mathematics and Neural Network


Authors: Wenhui Hou, Caiwen Niu

Pages: 986-995 

DOI: 10.46300/9106.2021.15.106     XML


Abstract: As an important link in product development, car style evaluation could ensure the quality of car style design, making the design more efficient, laying the foundation for production planners, production managers, and investment decision-makers in automobile manufacturing. The consumer-centered evaluation should accurately reflect the psychological cognition and subjective feelings of consumers. However, the current studies have not provided a unified evaluation standard, nor fully utilized the massive data on the evaluations made by consumers. Considering in advantages of fuzzy mathematics and neural network in processing massive data on consumer evaluations, this paper designs a consumer-oriented car style evaluation system based on these two techniques. Firstly, a scientific evaluation index system was designed for consumer-oriented car style evaluation, the index scores were classified into different levels, and a judgment matrix was constructed for indices on each layer and subject to consistency check. Next, absolute weights were assigned to alternatives, and the corresponding fuzzy membership functions were determined, producing a fuzzy comprehensive evaluation (FCE) model based on analytic hierarchy process (AHP) (AHP-FCE model) for car style evaluation. Furthermore, car styles were categorized by appearance structure, and the car style samples were parametrized for evaluation. Finally, particle swarm optimization (PSO) was improved, and then combined with backpropagation neural network (BPNN) into a classification model for consumer-oriented car style evaluation. The proposed consumer-oriented car style evaluation model was proved effective and superior through experiments. The results offer a reference for the application of the model in other evaluation scenarios.