Multi-layer Adaptive Optimizing Algorithm for Least Squares Support Vector Machines

A multi-layer adaptive optimizing parameters algorithm is developed for improving least squares support vector machines (LS-SVM), and a military equipment intelligent cost estimation model is proposed based on the optimized LS-SVM. The intelligent cost estimation process is divided into three steps in the model. In the first step, cost-drive-factor is needed to be selected, which is significant for costs estimation. In the second step, military equipment training samples within costs and cost-drive-factor set are learned by the LS-SVM. After learned, the model can be used for new equipment costs estimation. Aircraft costs become more expensive in recent years. Chinese military aircraft costs are estimation in the paper. The results show that the estimation costs by the new model are more closed to the true costs than that of traditional used method.