LS_SVM Based on DTW and Sliding Window Pruning Algorithm and its Application

Abstract

Compared with the traditional SVM, LS_SVM improves the speed of the large sample study,however ,LS_SVM loses its sparsity, which is an advantage of the the traditional SVM . For LS_SVM,almost all of the samples are support vectors .When a new sample added all samples are re-involved in the calculation, and that greatly increases the amount of the computation. In response to these issues,this paper presents an algorithm called the LSSVM sliding window pruning algorithm,which is based on DTW. With the combination of local modeling ,the algorithm selects the most similar samples with the current sample point in the process of the establishment of the on-line model of the batch process as the training samples .It can remove those samples that are relatively ineffective,which can restore LSSVM 's sparsity.Taking the cell concentration in the fermentation process of E. Coli for example, this paper proves that the method can establish a more accurate on-line prediction model in the case of a smaller number of samples . Compared with the off-line LSSVM ,the on-line model has a higher accuracy and a better dynamic adaptability.