关键词: 天气识别, 支持向量机, K近邻, 识别正确率

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 calculated, 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 carried 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