BP人工神经网络在一种铸铁生产上的应用

铸铁广泛应用于机械、矿山等行业，是工业生产的基础材料之一，稳定、提高其性能有着重要的意义。铸铁含有多种元素，元素之间交互作用，对其性能表现出复杂的非线形关系。人工神经网络能充分逼近任意复杂的非线形系统。本文把即人工神经网络用于一种铸铁的生产，它成功地根据材料成分预测了材料的性能，从而有助于优化设计，提高产品质量。降低铸铁的生产成本。

The use of BP artificial neural networks in the production of a kind of casting iron

casting irons having been widely used in machine, mining industry, etc. are one of the basic materials of industry. So it is very important to stabilize and improve its property. They often contain many elements, all of them affect the property of the material. The elements correlate, showing complicated non-linear results. Artificial neural networks can imitate any non-linear systems. The article shows how BP artificial neural networks is used in the production of a kind of casting iron. It successfully predicts the property of the material according to its composition. Thus it can help to optimize the designing of the material, reduce the cost of its production and improve the quality.