1) silicon content in hot metal([Si])
铁水含硅量([Si])
2) forecasting silicon content in molten iron
铁水Si含量预报
3) silicon content in molten iron
铁水含硅量
1.
With fuzzy theory and stochastic theory,a new intelligent model is developed to approach the random nonlinear dynamic system of the change of silicon content in molten iron,where noise influencing the fuzzy prediction system is thoroughly considered.
该文在考虑了具有模糊化和非模糊化的模糊逻辑系统用于高炉铁水含硅量[Si]时各种噪声干扰的同时,把模糊数学理论和随机系统理论结合在一起,建立了一种新的高炉铁水含硅量[Si]的智能预报模型,该模型是由非单值模糊化、模糊规则库、模糊推理机、特殊非模糊化构成的随机模糊神经网络逻辑系统。
2.
In this paper,ANN(Artificial Neural Network)method is applied to predict the silicon content in molten iron,Several variables have been selected,and a three layers BP(Background Propagation)neural network model is set up.
讨论了人工神经元网络（ＡＮＮ）方法在铁水含硅量预报上的应用策略。
4) silicon content in hot metal
铁水含硅量
1.
Prediction of silicon content in hot metal based on EMD-SVM nonlinear combined model;
EMD-SVM非线性组合模型对高炉铁水含硅量的预测
2.
It is essential to predict silicon content in hot metal accurately for the purpose of controlling blast furnace under good operation condition.
准确预测铁水含硅量是有效控制高炉的前提，人工智能专家系统已在铁水硅含量预测方面取得显著进展，但专家系统在知识获取方面存在不足。
3.
It decomposes the time series of original silicon content in hot metal to different layers through wavelet analysis.
先用小波变换将铁水含硅量的时间序列分解成不同的高频和低频层次,对不同层次构建支持向量机模型进行预测,然后通过序列重构得到原始时间序列的预测结果。
5) silicon content in molten iron
铁水硅含量
1.
Based on the algorithm of Kolmogorov entropy presented by Grassberger and Procaccia, as the time series data of silicon content in molten iron of No.
根据Grassberger和Procaccia提出的Kolmogorov熵计算方法 ,以山东莱芜钢铁集团公司 1号高炉和山西临汾钢铁集团公司 6号高炉测得的铁水硅含量时间序列为样本 ,计算了各自的Kolmogorov熵分别为 (0 14 5 3± 0 0 15 1)nats·h- 1 和 (0 15 5 3± 0 0 14 0 )nats·h- 1 ,并估计了两座高炉铁水硅含量可预测的时间尺度分别约为 6 88和 6 4 4h 。
6) silicon content in hot metal
铁水硅含量
1.
Application of SVM to prediction of silicon content in hot metal;
支持向量机在铁水硅含量预报中的应用
2.
The support vector regression based on the chaos particle swarm optimization algorithm for the prediction of silicon content in hot metal
铁水硅含量的混沌粒子群支持向量机预报方法
3.
A new approach to predict the silicon content in hot metal is based on Bayesian network.
应用贝叶斯网络对高炉铁水硅含量进行预测。
补充资料：氮化硅(si_(3)N_(2))基陶瓷制品
氮化硅(si_(3)N_(2))基陶瓷制品
氟化硅(s11N z)基陶瓷制品中国科学院上海硅酸盐研究所供稿
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条