Hyperspectral image classification using FPCA-based kernel extreme learning machine

Wei, Yantao; Xiao, Guangrun; He, Di; Chen, Hong; Tong, Mingwen; Zhao, Gang; Liu, Qingtang

doi:10.1016/j.ijleo.2015.07.184

Cited by 21 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A prediction model based on a kernel extreme learning machine (KELM) is established. [35][36] The concentration is influenced by many variables, including the flow rate and temperature of the live steam; the flow rate, temperature and concentration of the feed, and the temperature of the outlet liquid material and the secondary steam. These variables are taken as inputs of the prediction model.…”

Section: Time Registration Application Test Resultsmentioning

confidence: 99%

“…The industrial data with time registration and without time registration are applied to predict the concentration of the outlet mother liquor from the fourth flash evaporator, respectively. A prediction model based on a kernel extreme learning machine (KELM) is established . The concentration is influenced by many variables, including the flow rate and temperature of the live steam; the flow rate, temperature and concentration of the feed, and the temperature of the outlet liquid material and the secondary steam.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Data reconciliation strategy with time registration for the evaporation process in alumina production

et al. 2017

View full text Add to dashboard Cite

In real industrial production, many mass and heat transfer processes are influenced by high temperature, high pressure, and even strong acid or alkali conditions. In addition, some important variables cannot be measured and chemical compositions are analyzed offline with a long time delay, which leads to inaccurate measurements of the process data. In this paper, a layered data reconciliation (LDR) method based on time registration is proposed to improve the measurement accuracy and estimate unmeasured variables. Considering that the material cannot be tagged and tracked in process manufacturing, a temporal and spatial matching strategy for the process data is designed based on a time-correlation analysis matrix which is determined to describe the correlation of each time sequence in the data matrix. Then, a layered data reconciliation model with time registration is developed by reconciling the mass balance layer and the heat balance layer separately and stepwise, and the model is solved by the state transition algorithm. Meanwhile, regular terms and engineer's knowledge are introduced into the data reconciliation model to solve the problem of insufficient redundancy. The industrial verification results from the actual industrial evaporation process indicate that the accuracy of measured values is improved by using the proposed reconciliation strategy.

show abstract

Section: Time Registration Application Test Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%