Abstract:There are some problems in the prediction model of the determination of water COD by using UV-visible spectroscopy, such as low precision and slow convergence speed. This paper studied an optimization method based on particle swarm optimization algorithm in combination with least squares support vector machine algorithm, and introduced the principal component analysis (PCA) algorithm to reduce the dimension of the input data in order to improve the convergence speed of the model. PSO had the ability of fast convergence speed and global optimization. The penalty factor and the kernel function parameter of the traditional LSSVM model had been optimized by PSO to overcome the blindness of selecting parameters manually and disadvantages of LSSVM prediction model of low precision, poor robustness. LSSVM model and PSO_LSSVM model had been established, which the dimensionality of input data had not been reduced. PSO_LSSVM prediction model had been established, which the dimensionality of input data had been reduced by PCA. Comparisons were conducted by computing the evaluation standard of the convergence time, average relative prediction error (MRE) and root mean square error (RMSE), and result were that the prediction ability of PSO_LSSVM model which using PCA superior than other two. The algorithm of the model were achieved by C language which more easy to transplant, and laid the foundation for real-time, online determination of Water COD by using UV –visible spectroscopy.
汤斌, 赵敬晓, 魏彪, 蒋上海, 罗继阳, Vo Quang Sang, 冯鹏, 米德伶. 一种紫外-可见光谱检测水质COD预测模型优化方法[J]. 中国环境科学, 2015, 35(2): 478-483.
TANG Bin, ZHAO Jing-Xiao, WEI Biao, JIANG Shang-Hai, LUO Ji-Yang, Vo Quang Sang, FENG Peng, MI De-Ling. A method of optimizing the prediction model for the determination of water COD by using UV-visible spectroscopy. CHINA ENVIRONMENTAL SCIENCECE, 2015, 35(2): 478-483.