Sports close-fitting clothes fitness level prediction method based on probabilistic neural network
A technology of probabilistic neural network and prediction method, applied in the field of predicting the fit level of sports tights based on probabilistic neural network, to achieve the effect of improving performance and improving satisfaction
Active Publication Date: 2020-12-25
DONGHUA UNIV
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Problems solved by technology
However, no scholars have used probabilistic neural networks to predict the fit level of clothing (especially sports tights)
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[0024] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.
[0025] Embodiments of the present invention relate to a probabilistic neural network-based method for predicting the fitness level of sports tights, such as figure 1 As shown, the following steps are included: on the basis of collecting data such as the perceptual evaluation data of the fitness level of the sports tights, looseness (surplus), digital pressure and related fabric parameters, etc., the prediction data set of the f...
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The invention relates to a sports close-fitting clothes fitness level prediction method based on a probabilistic neural network. The method comprises the following steps: collecting perceptual evaluation data of real fitting sports close-fitting clothes; collecting the looseness, the digital pressure and the fabric data of the sports close-fitting clothes under virtual try-on; constructing a motion close-fitting clothes fitness level prediction model based on a probabilistic neural network, wherein the prediction model comprises a plurality of sub-models and a comprehensive model, and the sub-models predict local fitness levels of different feature parts respectively; enabling the comprehensive model to summarize the local fitness levels predicted by the sub-models to form an overall fitness level of the sports close-fitting clothes; and training the constructed exercise close-fitting clothes fitness level prediction model, and predicting new data by adopting the trained prediction model. The fitness level of the sports close-fitting clothes can be accurately and automatically predicted and controlled.
Description
technical field [0001] The invention relates to the field of clothing structure design, in particular to a probabilistic neural network-based method for predicting the fitness level of sports tights. Background technique [0002] Currently, innovative digital technologies, such as big data, virtual reality, cloud computing, and the Internet of Things (IoT), have gradually changed our daily lives. Consumers across the world are increasingly preferring to buy clothing online in e-stores instead of brick-and-mortar stores due to the many advantages of sharing convenience, discounts, and time saving. This trend provides new opportunities for the apparel industry. Unfortunately, the high return rate (nearly 30%), especially related to poor clothing fit, is one of the most important disadvantages of online shopping for clothing, which requires brands to spend extra time and money to process returned goods. clothing. Compared with other design attributes such as fashion style, s...
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IPC IPC(8): G06F30/20G06N3/04G06N3/08
CPCG06F30/20G06N3/08G06F2113/12G06N3/047Y02P90/30
Inventor 王竹君王建萍姚晓凤徐朔
Owner DONGHUA UNIV