伸展
发表于 2025-3-25 05:05:33
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我就不公正
发表于 2025-3-25 09:49:29
Canonical Variate Analysis Based Process Monitoring and Fault Diagnosisonical correlation analysis (CCA) is a further MVA method, which is also, in particular in the control community, known as canonical variate analysis (CVA) and recognized as a standard method for system identification.
Pageant
发表于 2025-3-25 12:39:15
Introduction, Preliminaries and I/O Data Set Models domains. Thanks to their intimate relations to automatic control systems, model-based schemes are widely accepted as a powerful technology in dealing with process monitoring and fault diagnosis for dynamic processes.
刺耳
发表于 2025-3-25 16:17:01
Economics of Justice and Peace in the WorldPrincipal component analysis (PCA) is a basic method of multivariate analysis (MVA) and plays, both in the research and application domains, an important role.
外来
发表于 2025-3-25 21:29:14
Springer Studies in Alternative EconomicsRoughly speaking, partial least squares (PLS) regression is a multivariate analysis method that constructs a linear regression between two data sets expressed in form of data matrices.
教育学
发表于 2025-3-26 01:30:05
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有花
发表于 2025-3-26 04:22:04
J. A. Canul-Polanco,O. M. JensenThe key of data-driven fault diagnosis schemes consists in a direct identification of key fault detection statistics like covariance matrices applied in the MVA or SIM based methods.
亲密
发表于 2025-3-26 09:18:45
IMP: A Simple Imperative LanguageIn the previous chapter, we have introduced the fault-tolerant architecture and the associated design parameters. In this chapter, we focus on the data-driven design of the state feedback gain matrix, an H-PRIO parameter of the fault-tolerant architecture, and its application to the observer-based control schemes.
演绎
发表于 2025-3-26 14:54:37
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enlist
发表于 2025-3-26 17:55:49
Application of Partial Least Squares Regression to Fault DiagnosisRoughly speaking, partial least squares (PLS) regression is a multivariate analysis method that constructs a linear regression between two data sets expressed in form of data matrices.