In this article, an intelligent pilling prediction model using back-propagation neural network model and an optimized model with genetic algorithm is introduced. Genetic algorithm is proposed in consideration of the initial weight and threshold of back-propagation artificial neural network, and further improves training speed and the accuracy for prediction pilling of polyester–cotton blended woven fabrics. The results show that the maximum numbers of training steps of the optimized model by genetic algorithm are reduced from 164 steps to 137 steps compared with that of back-propagation model. The training fitness of optimized model by genetic algorithm is improved from 0.914 to 0.945. The simulation fitness is increased from 0.912 to 0.987. And the root mean square error decreased from 1.0431 to 0.6842. The optimized model by genetic algorithm shows a better agreement between the experimental and predicted values.

中文翻译：

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使用人工神经网络模型预测涤棉混纺织物起毛起球

本文介绍了基于反向传播神经网络模型的智能起球预测模型和基于遗传算法的优化模型。考虑到反向传播人工神经网络的初始权重和阈值，提出了遗传算法，进一步提高了训练速度和涤棉混纺织物预测起毛起球的准确性。结果表明，与反向传播模型相比，遗传算法优化模型的最大训练步数从164步减少到137步。遗传算法优化模型的训练适应度从0.914提高到0.945。模拟适应度从 0.912 增加到 0.987。均方根误差从 1.0431 降低到 0.6842。