Results 81 to 90 of about 7,174 (250)
High‐Order Sliding‐Mode control for MIMO Systems
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT This paper extends Lyapunov‐based homogeneous high‐order sliding‐mode control to a class of uncertain non‐square multi‐input multi‐output (MIMO) nonlinear systems with a well‐defined vector relative degree. The considered systems admit a normal‐form representation with an uncertain but full‐row‐rank input‐gain matrix.
Jaime A. Moreno, Angel Mercado‐Uribe
wiley +1 more source
A Constrained Sparse Algorithm for Nonnegative Matrix Factorization
工程科学与技术, 2015 :Aiming at the lack of sparseness of factorization matrix in the nonnegative matrix factorization (NMF) algorithm,a new constrained NMF algorithm was proposed.A sparseness constraint was added to the original nonnegative matrix factorization (NMF ...
李臣明, 张师明, 李昌利
doaj
Multimode Process Monitoring Method Based on Multiblock Projection Nonnegative Matrix Factorization
Advances in Mathematical Physics, 2020 A multimode process monitoring method based on multiblock projection nonnegative matrix factorization (MPNMF) is proposed for traditional process monitoring methods which often adopt global model of data and ignore local information of data. Firstly, the
Yan Wang +5 more
doaj +1 more source
IEEE Access, 2020 Nonnegative matrix factorization-based image representation algorithms have been widely applied to deal with high-dimensional data in the past few years.
Zhenqiu Shu +4 more
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A new smooth failure criterion for concrete inspired by Lubliner's condition
Structural Concrete, EarlyView.Abstract A new failure criterion with 10 parameters is proposed, based on Lubliner's idea of joining two Drucker–Prager cones. The novelty lies in the way of introducing deviatoric shape variation: through two Podgórski's functions. This feature allows for improving plane stress cross‐section's compatibility with experimental data.
Inez Kamińska, Aleksander Szwed
wiley +1 more source
Similarity Learning-Induced Symmetric Nonnegative Matrix Factorization for Image Clustering
IEEE Access, 2019 As a typical variation of nonnegative matrix factorization (NMF), symmetric NMF (SNMF) is capable of exploiting information of the cluster embedded in the matrix of similarity.
Wei Yan +3 more
doaj +1 more source
Advances in independent component analysis and nonnegative matrix factorization [PDF]
, 2009 A fundamental problem in machine learning research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors.
Yuan, Zhijian
core
VIEW, EarlyView.In this work, we develop submicron‐resolution mapping of intracellular lipid elements (SMILE) as an extraction‐free vibrational spectroscopic imaging platform based on hyperspectral stimulated Raman scattering microscopy with a spectral analysis pipeline for pixel‐resolved lipid profiling.
Yihui Zhou +10 more
wiley +1 more source
Sparsity induced convex nonnegative matrix factorization algorithm with manifold regularization
Tongxin xuebao, 2020 To address problems that the effectiveness of feature learned from real noisy data by classical nonnegative matrix factorization method,a novel sparsity induced manifold regularized convex nonnegative matrix factorization algorithm (SGCNMF) was proposed ...
Feiyue QIU +3 more
doaj +2 more sources
Boosted unsupervised feature selection for tumor gene expression profiles
CAAI Transactions on Intelligence Technology, EarlyView.Abstract In an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples.
Yifan Shi +5 more
wiley +1 more source