NDLI: A soft sensor based on kernel PCA and composite kernel support vector regression for a flotation circuit

Content Provider	IEEE Xplore Digital Library
Author	Huizhi Yang Min Huang
Copyright Year	2010
Description	Author affiliation: Zhongshan Institute, University of Electronic Science and Technology of China, China (Huizhi Yang; Min Huang)
Abstract	A soft sensor was developed to estimate the concentrate grade and recovery rate of a flotation circuit. The algorithm uses kernel principal component analysis (KPCA) and composite kernel support vector regression (CK-SVR) to perform the estimation. Firstly, the flotation prior knowledge and KPCA are employed to reduce the dimension of input vector of CK-SVR. Then, considering that the characteristics of kernels have great impacts on learning and predictive results of SVR, a composite kernel SVR modeling method based on polynomial kernel and RBF kernel is adopted which hyperparameters are adaptively evolved by the particle swarm optimization (PSO) algorithm. Simulations using real operating data show that the soft sensor provides the necessary accuracy for a flotation circuit.
Starting Page	375
Ending Page	378
File Size	207227
Page Count	4
File Format	PDF
ISBN	9781424458455
e-ISBN	9781424458486
DOI	10.1109/ICACC.2010.5487084
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	KPCA CK-SVR Circuit simulation Artificial neural networks Predictive models Sensor phenomena and characterization Minerals Particle swarm optimization PSO flotation circuit Support vector machines soft sensor Polynomials Kernel Principal component analysis
Content Type	Text
Resource Type	Article

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