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钢铁联合企业主要是根据铁矿石的化学成分进行铁矿石的配比。在配比之前,首先对铁矿石的化学成分做实验分析,然后对铁矿石进行优化配比。由于配矿过程耗费时间长、高成本以及配料的单一性,因此不具广泛性,而模糊神经网络能混匀配料,运用改进的网络算法计算速度比较快,不会耽误生产,还可以降低成本,基于这些优点,引入模糊神经网络来预测烧结矿的技术经济指标。本文针对成品率进行预测,并运用VisualC++使用C/C++语言的Win32应用程序集成开发环境,开发基于模糊神经网络烧结矿技术经济指标的预测程序。
Iron and steel conglomerate is mainly based on the chemical composition of iron ore iron ore ratio. In the ratio before the first chemical composition of iron ore to do the experimental analysis, and then optimize the ratio of iron ore. Due to the long process time, high cost and single ingredient of the ore blending process, it is not extensive. However, the fuzzy neural network can mix the ingredients and the improved network algorithm can calculate the speed relatively quickly without delay in production, and the cost can be reduced. Based on these advantages, the introduction of fuzzy neural network to predict the technical and economic indicators of sinter. In this paper, we predict the yield and use Visual C ++ Win32 application integrated development environment using C / C ++ language to develop the forecasting program based on economic indicators of the fuzzy neural network sinter technology.