Abstract:

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.

Key words: support vector machines, Markov chain, displacement time series, particle swarm optimization