计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2009年
22期
212-215
,共4页
污染预测%支持向量机%在线学习%增量式学习
汙染預測%支持嚮量機%在線學習%增量式學習
오염예측%지지향량궤%재선학습%증량식학습
pollution prediction%Support Vector Machine(SVM)%online learning%incremental learning
传统支持向量机基于批量训练方法,无法适应环境污染预测中的海量数据与实时性要求.在分析研究一种典型的在线支持向量机回归算法[4]的基础上,指出原算法在训练过程中存在样本重复移动问题,导致模型训练速度下降.提出一种改进算法,消除霹复移动问题.实验结果表明,该改进在线支持向量机算法建模精度高,训练速度较原算法有显著提高.
傳統支持嚮量機基于批量訓練方法,無法適應環境汙染預測中的海量數據與實時性要求.在分析研究一種典型的在線支持嚮量機迴歸算法[4]的基礎上,指齣原算法在訓練過程中存在樣本重複移動問題,導緻模型訓練速度下降.提齣一種改進算法,消除霹複移動問題.實驗結果錶明,該改進在線支持嚮量機算法建模精度高,訓練速度較原算法有顯著提高.
전통지지향량궤기우비량훈련방법,무법괄응배경오염예측중적해량수거여실시성요구.재분석연구일충전형적재선지지향량궤회귀산법[4]적기출상,지출원산법재훈련과정중존재양본중복이동문제,도치모형훈련속도하강.제출일충개진산법,소제벽복이동문제.실험결과표명,해개진재선지지향량궤산법건모정도고,훈련속도교원산법유현저제고.
Traditional Support Vector Machine(SVM), which based on batch training, can't satisfy the real-time requirement of environmental pollution prediction with large scale data. With the analysis of a typical kind of online support vector regression algorithm, this paper indicates that repeated sample move exists in the training process would lead to decrease the training speed, and proposes an improved algorithm. Simulation and analysis results show that the proposed algorithm performs high modeling precision, and training speed is increased remarkably compared with the aforementioned algorithm.
