烟气应用计算机信息加强技术,基于支持向量机的烟气成分建模研究-计算机应用技术专业论文.docx...-优快云博客

本文探讨了基于支持向量机（SVM）的锅炉燃气成分建模方法。SVM是一种有效的机器学习技术，尤其适用于小样本和高维问题。针对最小二乘支持向量机（LS-SVM）存在的样本稀疏性问题，提出了一种新的基于边界样本的LS-SVM算法，提高了计算速度并改进了训练数据的选择。此外，为了解决不平衡样本问题，提出了一种新的LS-SVM算法，提升了对不平衡样本的分类精度，特别是在分析燃气中NOx成分建模的应用上。最后，结合稀疏算法和粒子群优化提出了新的LS-SVM建模算法，为复杂工业系统的不平衡数据建模提供了一种途径。

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摘要

Subject:Research on Gas Ingredient Modeling Based On Support VectorMachine Specialty:The Technology of Computer Application Name:Zhao Hui Supervisor: Huang Jing-tao Associate Professor

ABSTRACT

Gas ingredient of boiler combustion directly reflects the combustion states, and it is an important indicator of the operation and economic combustion of the boiler, therefore, it’s necessary to analysis the gas ingredient.

Support vector machine (SVM) is a new machine learning method based on

statistical theory. SVM can effectively solve the practical problem with small sample size and high dimension. Now, it has been widely used to solve the problem of regression estimation, pattern recognize and so on. Based on these advantages, SVM has provided a theory foundation and ideas for the analysis of gas ingredient.

A data-based nonlinear system was proposed by using SVM theory in this paper,

and the model was built for the analysis of gas ingredient which was hardly online measured. Firstly, in order to solve the problem of the sparseness lacking in the least squares support vector machines (LS-SVM). A new least squares support vector machines based on the boundary samples was proposed. The new algorithm can properly obtain the sparse solutions to the LS-SVM, and the speed of computing was also improved. It also provided a new and effective method of selecting the training data for gas ingredient , Secondly, according to the relations of support vector, center distance ratio, incremental learning and margin vector, a new LS-SVM algorithm for the unbalanced samples was proposed. This method effectively improved the classification accuracy of LS-SVM for the unbalanced samples. The method was used

to build model for

NOx

in the gas ingredient, and provided a way to build model for

unbalanced data in the complex industrial systems, At last, a new LS-SVM modeling algorithm was proposed by combining sparsity algorithm and particle swarm opti