Quantum-behaved Particle Swarm Optimization Based Study On Physical Properties Of Functional Thin Film With Support Vector Regression

Regression analysis is one of the most widely used branch in the mathematical statistics. The traditional regression algorithms are all based on the asymptotic theory of classical statistical mathematics, where the statistical rule is exposed only when the number of samples approximating infinite. For that weakness, Vapnik and co-workers proposed the statistical learning theory (SLT) and support vector machine (SVM) in 1995. Even in the case of small-sample database, SVM could still be adopted in classification and regression analysis with preferable generalizing performance, so that it has been extensively applied to tackle many real-world problems in various fields. Particle swarm optimization (PSO) is a novel evolutionary computing approach based on swarm intelligence theory. From the view of quantum mechanics, Sun et al. proposed the quantum-behaved particle swarm optimization (QPSO) via the research on particle convergence behaviour. Because the quantum-behaved particles meet completely different aggregation property, the optimal solution could be searched out in the whole solution space, so that QPSO algorithm is superior to all the kinds of previous proposed PSO algorithms.In this thesis, many traditional and modern regression approaches are employed for regression analysis based on the experimental datasets of functional thin film materials. It is focused on the application of SVR method combined with QPSO for physical properties prediction on some functional materials including nitrogen-oxygen compound dielectric thin film, proton exchange membrane fuel cell, Bi-system superconductivity material and Co₃O₄ nanoparticles. Meanwhile, the multifactor analysis, process parameter optimization and sensitivity analysis is carried out based on the established QPSO-SVR models.The main contents of this thesis are as follows:1. The current status and problems on the functional thin film material research were generally introduced. The regression principles of popular regression methods such as multivariable linear regression (MLR), partial least square regression (PLS), probabilistic neural network (PNN) and extreme learning machine (ELM), were reviewed briefly. The principle of SLT and SVR algorithm were described in detail.2. The principle, algorithm and development of PSO were introduced, especially for QPSO method. Moreover, some optimization methods, including genetic algorithm (GA), differential evolution (DE), scatter search (SS), grid search algorithm (GSA), simulated annealing (SA) and ant conoly optimization (ACO) were reviewed. The advantages and disadvantages of these algorithms were also summarized.3. Based on the experimental datasets of nitrogen-oxygen compound dielectric thin film, proton exchange membrane fuel cell, Bi-system superconductivity material and Co₃O₄ nanoparticles, QPSO-SVR and other regression approaches were employed to model and predict the physical properties, as well as their predicted results and generalizing performance were compared.4. Based on the established QPSO-SVR models, the multi-factor analyses on the proton exchange membrane fuel cell, Bi-system superconductivity material and Co₃O₄ nanoparticles were conducted and the optimal process conditions were proposed.The studies of above demonstrated that the prediction precison of QPSO-SVR was superior to those of other regression methods including multivarivate linear regression, neural network and so on, and its generalization ability surpasses those of them. The results suggest that SVR is an effective and powerful technique, and it may be further developed to be a potential application tool in research and development of novel functional thin film materials.