本文提出了一种新的基于神经网络软测量模型及算法,为复杂测量系统中软测量建模提供了一种有效的途径。
This paper introduces a new model and algorithm of soft measurement based on Neural Network. It can solve the problem of soft measurement modeling in complicated.
利用软测量的思想,采用神经网络建立软测量模型和小波分析进行数据处理,以克服传统方法的不足,提高出口带钢厚度预测的精度。
The idea of soft-measurement is used in our work and neural network and wavelet transform are adopted to overcome deficiency of traditional method and improve forecast accuracy of exit thickness.
本文采用主成分分析技术对过程数据降维,然后用降维后的数据训练神经网络,建立软测量模型。
Then, USES PCA to reduce the dimensions of process data, trains the neural network with that data, and establishes the soft sensor.
本文通过检测电流信号基于随机模糊神经网络建立了刀具磨损量的软测量模型。
Through measuring the electric current signal, the soft sensing model used for tool wear estimation based on stochastic fuzzy neural network(SFNN) is presented in this paper.
笔者采用PCA BP神经网络建立了蒸发过程出料液浓度软测量预测模型。
The soft-sensing prediction model of product concentration of citric acid double-effect evaporator process is founded on the PCA-BP neural network.
预测结果表明,采用BP神经网络建立软测量模型是可行的,所建模型是合理的。
The result forecasted makes it clear that to creating soft-measuring model by BP neural network is doable and the model is reasonable.
以径向基函数神经网络作为软测量模型,在软测量建模中引入正则化学习算法。
Using RBF (Radial Basis Function) network as the soft-sensing model, its natural to introduce regularization learning algorithm.
基于线性回归和RBF神经网络构建火道软测量模型,为控制建立温度反馈环节。
A flue temperature soft measurement model based on linear regression and RBF neural network was built to establish temperature feedback control.
该方法由三部分组成:主元分析pca、时间延迟神经网络、软测量模型的在线校正。
It is composed of three elements: PCA, time-delay neural network and model updating, where the offline model is trained through the algorithm GABP.
采用的方法中构建了基于自适应小波神经网络的油水两相流质量流量软测量模型,实现了两相混合总质量流量的测量。
A soft-measurement model based on the adaptive wavelet neural network was developed to measure the total mass flow-rate of oil-water two-phase flow.
采用的方法中构建了基于自适应小波神经网络的油水两相流质量流量软测量模型,实现了两相混合总质量流量的测量。
A soft-measurement model based on the adaptive wavelet neural network was developed to measure the total mass flow-rate of oil-water two-phase flow.
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