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The PSO SVM model with polynomial kernel function outperformed other SVM models.
An analytical model with polynomial nonlinearity in the contact between rotors and stators is considered.
This talk will describe a model reduction approach that combines lifting the introduction of auxiliary variables to transform a general nonlinear model to a model with polynomial nonlinearities--with proper orthogonal decomposition.
A 'subblockwise' linear channel model with polynomial fitting is introduced to facilitate the separated channel estimation of S → R and R → D. 3.
We consider first a very simple model with polynomial expressions instead of ionic currents, Hindmarsh Rose (HR) [10], that has the essential components, a fast spiking subsystem and a slow adaptation variable.
A 'subblockwise' linear channel model with polynomial fitting is introduced to facilitate the separated channel estimation of S → R and R → D. Optimal ST signals at both S and R are optimized w.r.t.
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The results, that polynomials are better approximations than splines, were quite surprising since the architecture of a single stationary α-helix is based on a model with polynomials or polynomial splines with one or two kinks (extremal values).
An alternative for developing meta-models with polynomial functions in high-dimensional problems is offered by the newly emerged low-rank approximations (LRA) approach.
We then describe concrete applications to lattice models with polynomial interactions (i.e., the discretePd-models of Euclidean quantum field theory).
This paper examines various model structures including ARX and step-response models with polynomial or spline nonlinearities and their corresponding identification strategies.
The final RC prediction models were SVR models with polynomial kernels (of degree three) comprising only features with an average z-score of 1.5 and above (about 7% of the total).
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