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The Evaluation of Fabric Prickle Based on BP Neural Network

机译:基于BP神经网络的Fabric Prickle评估

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A three-layer BP neural network model was established by relating subjective evaluation of fabric prickle level and 16 objective parameters from KES-FB system. The elastic gradient decrease method was adopted for network training to achieve the preset precision of the model which was later applied to fabric prickle level evaluation. Results from this method gave a considerably accuracy compared with actual subjective results which implied a compatibility between BP neural network and traditional subjective evaluation.
机译:通过对KES-FB系统的主观评估和16个物理参数相关的主观评估建立了三层BP神经网络模型。采用弹性梯度降低方法进行网络培训,以实现模型的预设精度,后来应用于Fabric Prickle Level评估。与暗示BP神经网络与传统主观评价之间的兼容性相比,该方法的结果具有相当大的准确性。

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