改进k近邻和支持向量机相融合的天气识别-计算机工程与应用.pdf

148 2014 ，50（14） Computer Engineering and Applications 计算机工程与应用 改进K 近邻和支持向量机相融合的天气识别 1 1 2 张红艳 ，李茵茵 ，万 伟 1 1 2 ZHANG Hongyan , LI Yinyin , WAN Wei 1.广州中心气象台，广州 510080 2.广东广播电视大学 计算机技术系，广州 510091 1.Guangzhou Central Meteorological Observatory, Guangzhou 510080, China 2.Department of Computer Technology, Guangdong Radio TV University, Guangzhou 510091, China ZHANG Hongyan, LI Yinyin, WAN Wei. Weather identification based on improved K nearest neighbor and sup- port vector machine. Computer Engineering and Applications, 2014, 50 （14）：148-151. Abstract ：The weather which is affected by many factors is changeable and uncertain, single model is difficult to obtain high identification rate, therefore, this paper proposes a weather identification model （IKNN-SVM ）based on improved K nearest neighbor and support vector machine. Firstly, the distance between of the testing sample and a hyper plane is cal- culated, then the distance is compared with the threshold, if distance is greater than the threshold, then support vector machine is used to identify the weather, otherwise the K nearest neighbor algorithm is used to identify the weather, and the sample density is introduced to solve the defects of K nearest neighbor algorithm, finally the simulation experiment is car- ried out to test on the performance of model. The simulation results show that, compared with the single KNN or SVM, IKNN-SVM has improved weather identification correct rate and can overcome the defects of the single model. Key words ：weather identification; support vector machine; K nearest neighbor; recognition correct rate 摘 要：天气受到多种因素综合影响，具有时变性和不确定性，单一模型难以获得较高的识别正确率，为此，提出一 种改进K 近邻和支持向量机相融合的天气识别模型（IKNN-SVM ）。首先计算待识别样本与超平面间距离，然后将 距离与预设阈值进行比较，如果大于阈值，则采用支持向量机对天气进行识别，否则利用K 近邻算法对天气进行识 别，并引入样本密度对K 近邻算法进行改进，最后采用仿真实验对模型性能进行测试。仿真结果表明，相对于单一 的KNN 或SVM ，IKNN-SVM 提高了天气识别正确率，较好地克服单一模型存在的缺陷。 关键词：天气识别；支持向量