基于支持向量机的电能质量综合评价-本科毕设论文.docVIP

学号： 常 州 大 学 毕业设计（论文） （2012届） 题 目 学 生 学 院 专业班级 校内指导教师 专业技术职务 校外指导老师 专业技术职务 二○  基于支持向量机的电能质量综合评价统计学习理论是在研究小样本统计估计和预测的过程中发展起来的一种新兴的理论，它试图从更本质的层面上来研究机器学习的问题。作为统计学习理论的必威体育精装版发展和结构风险最小化准则的具体体现，支持向量机（SVM）具有全局最优、结构简单、推广能力强等优点，在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势，并能够推广到函数拟合等其他机器学习问题中。本文研究了SVM在电能质量分析中的应用，提出了一种包含综合集成赋权法和支持向量机的电能质量评价方法。在综合集成赋权法中,层次分析法确定主观权重,改进拉开档次法确定客观权重,基于加法原理将两者结合确定综合权重。由此产生支持向量机的初始训练数据,通过训练支持向量机能够得到电能质量综合评价模型及综合评价结果。算例结果表明,提出的电能质量评价策略保留了支持向量机在处理小样本、非线性问题时的独特优势,能快速得到合理的评价结果,并具有良好的泛化能力。 电能质量；支持向量机(SVM)；综合集成赋权；层次分析法； Power Quality Comprehensive Evaluation based on Support Vector Machine Abstract：Statistical learning theory is newly developed theory for studying the statistical estimation and prediction problem based on small number of samples. It studies the nature of machine learning. As the latest development of statistical learning theory and the embodiment of structural minimization criterion, Support Vector Machines(SVM) provide efficient and powerful algorithms that are capable of dealing with high dimensional input features and with theoretical bounds on the generalization error and sparseness of the solution provided by statistical learning theory. SVM has few free parameters requiring tuning is simple to implement, and are trained through optimization of a convex quadratic cost function, which ensures the global optimization of the SVM solution. Furthermore, SVM-based solutions are sparse in the training date and defined only by the most informative training points. SVM presents a lot of advantages for solving the small samples, nonlinear and high dimensional pattern recognition, as well as other machine-learning problems such as function fitting. A novel compr