Qiusheng Lian

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In this paper, a new method is proposed to identify solid oxide fuel cell using extreme learning machine–Hammerstein model (ELM–Hammerstein). The ELM–Hammerstein model consists of a static ELM neural network followed by a linear dynamic subsystem. First, the structure of ELM–Hammerstein model is determined by Lipschitz quotient criterion from input–output(More)
At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of(More)
In a wide variety of imaging applications (especially medical imaging), the theory of compressed sensing has shown it is surprisingly possible to reconstruct the entire original image from a partial set or subset of the Fourier transform of an image, if the image has a sparse or nearly sparse representation in some transform domain. Recently many fast and(More)