AN Aeroengine Forecasting Model and its Applications
Haojun Xu · Fire Control and Command Control · 2007
A new support vector forecasting model based on chaos theory is presented in this paper.It adopts support vector machines as nonlinear forecaster and determines network's input variable number through computing reconstruct phase space's saturated embedding dimension.The maximum effective forecasting steps is determined by computing chaos time series' largest Lyapunov exponent.It makes use of support vector machines' strongly nonlinear mapping ability,and network's structure is optimally auto-created.Application results in aeroengine show that the presented method possesses much better precision.