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首页> 外文期刊>Journal of Applied Polymer Science >A SIMPLE NEURAL NETWORK BASED MODEL APPROACH FOR NYLON 66 FABRICS USED IN SAFETY RESTRAINT SYSTEMS - A COMPARISON OF TWO TRAINING ALGORITHMS
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A SIMPLE NEURAL NETWORK BASED MODEL APPROACH FOR NYLON 66 FABRICS USED IN SAFETY RESTRAINT SYSTEMS - A COMPARISON OF TWO TRAINING ALGORITHMS

机译：基于神经网络的安全约束系统用尼龙66纤维的简化模型方法-两种训练算法的比较。

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Airbag technology relies on woven fabrics as the material of construction and particularly on knowledge pertaining to the fabric's permeability as a function of pressure drop, inflation temperature of the gas, fabric weave, fiber denier, and biaxial stress-strain relationships under biaxial conditions. While fabric permeability can be quantified by actual experimental measurements, the number and nonlinearity of the variables involved make the experiments time- and cost-intensive. Moreover, interpolations within a given data set can yield questionable results. In this study, a very simple feed forward neural network architecture was used with a training rule involving a nonlinear optimization routine for updating weights of the proposed network. This training was compared to the training with an error-back propagation routine. During this training, the ANN is introduced to data that contain the actual cause and effect patterns, with adjustments being made by changes in weighting factors until the errors in the output variables are minimized. Once trained, ANN can ascertain the essentials of the relationships and assimilate henceforth. In this study, after the initial training, the ANN was tested on additional data which were not part of the training processes. The predictions of the proposed trained network agreed very well with the new experimental data. On this basis, the proposed ANN model appears to be an effective tool for modeling airbag fabric behavior. This ANN model can assimilate relationships between different variables from the real-world data and does not require extensive normalizing of the process data like a back-propagation algorithm. Once trained, only fractions of a second are needed for information assimilation and output generation. This coupled with simplicity of use and accuracy of predictions from the real-world data make this ANN model attractive for on-line applications. (C) 1995 John Wiley & Sons, Inc. [References: 16]
机译：气囊技术依赖于机织织物作为结构材料，尤其是与织物渗透性有关的知识，该渗透性是压降，气体膨胀温度，织物组织，纤维旦数和双轴条件下双轴应力-应变关系的函数。尽管可以通过实际的实验测量来量化织物的渗透性，但是所涉及变量的数量和非线性使得该实验耗费时间和成本。此外，给定数据集内的插值会产生可疑的结果。在这项研究中，使用了非常简单的前馈神经网络架构，并使用了包含非线性优化例程的训练规则来更新拟议网络的权重。将此训练与带有错误反向传播例程的训练进行了比较。在此培训期间，将ANN引入包含实际因果模式的数据，并通过更改权重因子进行调整，直到将输出变量中的误差最小化为止。一旦受过训练，人工神经网络就可以确定这种关系的本质并从此吸收。在本研究中，在最初的训练之后，对ANN进行了测试，这些数据不是训练过程的一部分。拟议的训练网络的预测与新的实验数据非常吻合。在此基础上，提出的人工神经网络模型似乎是建模安全气囊织物行为的有效工具。这种ANN模型可以吸收现实数据中不同变量之间的关系，并且不需要像反向传播算法那样对过程数据进行广泛的标准化。一旦经过培训，信息吸收和输出生成只需几分之一秒。再加上使用的简便性和来自真实数据的预测准确性，使得该ANN模型对在线应用很有吸引力。 （C）1995 John Wiley＆Sons，Inc. [参考：16]

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