Proofs of Lemma 1, Theorem 2, Lemma 3, and Lemma 4, descriptions of the 2-phase algorithm and the main algorithm, and some figures for chemical graphs and upper and lower feature vectors used in the computational experiment are given in this supplemen (PDF 2 MB).
The proposed heuristic optimisation approach relies on the rank of the cross-covariance matrix between the observations of the target variables and the observations of the sensor measurement variables obtained from simulations using the computational model of an experiment.
Using the computational model of the experiment, an inverse solver was developed.
Using the computational model of the experiment, an inverse solution was developed (as detailed in Text S1).
The computational experiment using the crude oil spot price of West Texas Intermediate (WTI) verifies the effectiveness of the proposed LSSVR ensemble learning paradigm with uncertain parameters compared with some existing LSSVR variants (using other popular parameters selection algorithms), in terms of prediction accuracy and time-saving.
Throughout the computational experiment, the parameters used in Algorithm 3.1 were set as (gamma=0.6), (sigma=0.8), (theta=1.5), (eta_{0}=0.3), (beta=1).
The computational experiments were done using CPLEX 12, making use of a single CPU processor core with 4GB of memory.
Throughout the computational experiments, the parameters used in those algorithms were sets as (epsilon= 10^{-6}), (rho_{n} = 1), (alpha_{n} = frac{1}{2}), (beta_{n} = frac{1}{n+3}) and (gamma_{n} = 1-alpha_{n} -beta_{n}) for all (n=0,1,2,ldots) .
This section discusses our experimental results as well as obtained insights after evaluating the performance of our proposed method by using exhaustive computational experiments in diverse polygonal maps with both convex and non-convex topology.
Using computational experiments, the authors determined the likely damage criterion (dislocation-based model) and found several effects that can positively influence the mechanical response and strength of UFG titanium (homogeneity of grain sizes, dispersoids/precipitates in grain boundaries and initial dislocation density in grain boundaries).
