Journal ArticleDOI
Nonlinear principal component analysis—Based on principal curves and neural networks
Dong Dong,Thomas J. McAvoy +1 more
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TLDR
In this paper, a nonlinear principal component analysis (NLPCA) method which integrates the principal curve algorithm and neural networks is presented. But when applied to data sets the algorithm does not yield an NLPCA model in the sense of principal loadings.About:
This article is published in Computers & Chemical Engineering.The article was published on 1996-01-01. It has received 635 citations till now. The article focuses on the topics: Kernel principal component analysis & Sparse PCA.read more
Citations
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Journal ArticleDOI
Data-driven Soft Sensors in the process industry
TL;DR: Characteristics of the process industry data which are critical for the development of data-driven Soft Sensors are discussed.
Journal ArticleDOI
Survey on data-driven industrial process monitoring and diagnosis
TL;DR: A state-of-the-art review of the methods and applications of data-driven fault detection and diagnosis that have been developed over the last two decades are provided to draw attention from the systems and control community and the process control community.
Journal ArticleDOI
Nonlinear process monitoring using kernel principal component analysis
TL;DR: In this article, a new nonlinear process monitoring technique based on kernel principal component analysis (KPCA) is developed, which can efficiently compute principal components in high-dimensional feature spaces by means of integral operators and nonlinear kernel functions.
Journal ArticleDOI
Multiscale PCA with application to multivariate statistical process monitoring
TL;DR: Multiscale Principal Component Analysis (MSPCA) as mentioned in this paper combines the ability of PCA to decorrelate the variables by extracting a linear relationship with that of wavelet analysis to extract deterministic features and approximately decorrelation of autocorrelated measurements.
Journal ArticleDOI
Review of Recent Research on Data-Based Process Monitoring
TL;DR: The natures of different industrial processes are revealed with their data characteristics analyzed and a corresponding problem is defined and illustrated, with review conducted with detailed discussions on connection and comparison of different monitoring methods.
References
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Journal ArticleDOI
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book
Principal Component Analysis
TL;DR: In this article, the authors present a graphical representation of data using Principal Component Analysis (PCA) for time series and other non-independent data, as well as a generalization and adaptation of principal component analysis.
Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Journal ArticleDOI
Robust Locally Weighted Regression and Smoothing Scatterplots
TL;DR: Robust locally weighted regression as discussed by the authors is a method for smoothing a scatterplot, in which the fitted value at z k is the value of a polynomial fit to the data using weighted least squares, where the weight for (x i, y i ) is large if x i is close to x k and small if it is not.