In this paper, a structural illumination based technology for microscopic surface topography measurement is investigated, in which only one shot structural illumination image is grabbed and a more efficient optical sectioned image reconstruction algorithm based on Hilbert transform was proposed. Compared with other methods, the technology can avoid strip artefacts problems of in-focus images resulting from the sinusoidal phases mismatch in spatial domain in conventional three-step phase-shifting since the phase-shifting steps decreases from three to one, and the measurement time is decreased effectively. The experimental testing is carried out to verify the feasibility and its measurement accuracy.
A novel backward elimination algorithm (BEA) based on the energy contributions of the non-orthogonal (coupled) regressor vectors is introduced for radial basis function (RBF) neural network construction. This algorithm builds RBF model by eliminating the minimum contribution regressor term among all candidates according to mean predictedresidual- sums-of-squares (PRESS) error. It first generates an initial model using computationally affordable batch learning and then updated it by a sequent learning with new training samples arriving. During the whole learning, the network architecture always remains the most optimal. This also can assure a better RBF network even if the RBF original basis is non-orthogonal. The effectiveness of new algorithm is demonstrated by the simulated results.
This paper developed two learning procedure, respectively, based on the orthogonal least squares (OLS) method and
the "Innovation-Contribution" criterion (ICc) proposed newly. The orthogonal use of the stepwise-regression algorithm
of the ICc mages the model structure independent of the selected term sequence and reduces the cluster region further as
compared with orthogonal least squares (OLS). as the Bayesian information criteria (BIC) method is incorporate into the
clustering process of the ICc, except for the widths of Gaussian functions, it has no other parameter that need tuning ,but
the user is required to specify the tolerance ρ, which is relevant to noises and will be difficult to implement in the real
system, for the OLS algorithm. The two algorithms are employed to the Radial Basis Function Neural Networks
(RBFNN) to compare its performance for different noise nonlinear dynamic systems. Experimental results show that
they provide an efficient approximation to the required results for fitting models, but the clustering procedures of the ICc
is substantially better solutions than does the OLS.
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