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An Overview on Application of Neural Networks in Fabric Engineering

机译：神经网络在织物工程中的应用概述

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An Artificial Neural Networks (ANN) is an information processing paradigm that is inspired by the way biological nervous system, such as brain, process information. ANN consists of many simple computational neural units connected to each other. An input is presented to some (or all) of its input units, this input vector is propagated through the whole network and finally, some kind of output is splitted out. ANN's real power is on its ability to learn, that is, the function is not constant but can be changed dynamically. Neural networks perform computation in a very different way than conventional computers, where a single central processing unit sequentially dictates every piece of the action. Neural networks are built from a large number of very simple processing elements that individually deal with the pieces of a big problem. Today ANN's are being applied to an increasing number of real-world problems of considerable complexity. They are good pattern recognition engines and robust classifiers, with the ability to generalizing making decisions about imprecise input data. They offer ideal solutions to a variety of classification problems such as speech, character and signal recognition, as well as functional prediction and system modeling where the physical processes are not understood or are highly complex. The ANN, inspire of being a new entrant in this field, has notched up a number of papers to its credit. In the area of fabric - property prediction, traditionally subjective areas like handle and drape have received considerable attention.
机译：人工神经网络（ANN）是一种信息处理范例，它受到诸如大脑等生物神经系统处理信息的方式的启发。人工神经网络由许多相互连接的简单计算神经单元组成。输入被提供给它的某些（或全部）输入单元，该输入向量在整个网络中传播，最后，某种输出被分离出来。 ANN的真正力量在于它的学习能力，即功能不是恒定的，而是可以动态更改的。神经网络以与传统计算机非常不同的方式执行计算，在传统计算机中，单个中央处理单元顺序指示每个动作。神经网络是由大量非常简单的处理元素构建而成的，它们分别处理一个大问题。如今，人工神经网络已被应用于越来越多的复杂性很高的现实世界中。它们是良好的模式识别引擎和强大的分类器，能够对不精确的输入数据做出决策。它们为各种分类问题（例如语音，字符和信号识别以及功能预测和系统建模）提供了理想的解决方案，这些物理问题是无法理解或高度复杂的。人工神经网络（ANN）希望成为该领域的新成员，它已经发表了许多论文，值得称赞。在织物的特性预测领域，传统的主观领域（例如手感和悬垂性）受到了极大的关注。

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