摘要
结合支持向量机和马尔可夫链,提出了一种新的位移时序预测模型——支持向量机-马尔可夫链预测模型(SVM-MC)。通过对实测位移值的学习,利用经粒子群算法优化的支持向量机对位移时间序列的宏观发展趋势进行滚动预测;在此基础上应用马尔可夫链确定位移时序的状态转移概率矩阵,通过对状态的划分、实测值与支持向量机拟合值的绝对误差及相对误差等指标的分析,实现了对预测结果的改进。将该模型应用到某工程永久船闸高边坡的位移时序预测中,结果表明,该模型具有科学可靠、预测精度高的优点,在岩土体位移时序预测中具有有一定工程应用价值。
A new displacement time series predicting model was proposed by combining the support vector machines and Markov Chain, named as support vector machines-Markov chain （SVM-MC） model. Through studying the measured displacements, SVM optimized by particle swarm was used to dynamically forecast the trend of macro development. Markov chain was applied to compute state transition probability matrix. By classifying system state and calculating absolute error and relative error between measured values and SVM fitting values, the predicting results are improved. The model was used to predict displacement time series of a high slope of a permanent shiplock. The engineering case studies indicate that the model is scientific and reliable; and there is engineering practical value for displacement time series predicting.
出处
《岩土力学》
EI
CAS
CSCD
北大核心
2010年第3期944-948,共5页
Rock and Soil Mechanics
基金
国家自然科学基金重点资助项目(No50539110)
国家科技支撑计划(No2008BAB29B01)
国家科技支撑计划(No2006BAB04A02)
国家自然科学基金资助项目(No50909038)
关键词
支持向量机
马尔可夫链
位移时间序列
粒子群优化
support vector machines
Markov chain
displacement time series
particle swarm optimization
