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A new monitoring method based on kernel entropy component analysis KECA) is proposed for nonlinear chemical process.
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Several functions are constructed for evaluating the significance of features based on kernel approximation and fuzzy entropy.
Additionally, PDF estimators based on kernel functions are also developed.
However, some other feature extraction methods have put forward new ways which are based on kernels.
A new particle swarm optimization method based on the best kernel entropy component number theory with inertia weight and dynamic accelerating constant (BWCPSO) is proposed to optimize the kernel parameter.
A probability value is calculated for each partition to determine the entropy based on Shannon's entropy [ 28]. 4. Parametric graph entropy measures called Dehmer-entropy: To determine the entropy, measures of this class [ 8, 10], assign a probability value to each vertex of a graph, using so-called information functionals (IFs).
To this end, we first define the local kernel alignment based on centralized kernel alignment.
This definition of entropy is based on Shannon's entropy, which is a standard measure for estimating the uncertainty of a given signal in Information Theory (Schneider 2010).
This method is based on four entropy features for classification: phase entropy 1 (S1), phase entropy 2 (S2), approximate entropy (ApEn), and sample entropy (SampEn).
The estimates are based on the entropy methods of Dudley and Sudakov combined with a precise analysis of the relevant distance functions and covering numbers using off-diagonal asymptotics of Bergman Szegö kernels.
This is based on the relationship existing between entropy and exergy, either for heat interaction (thermal exergy based on thermal entropy) or for work interaction (mechanical exergy based on mechanical entropy).
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