Abstract
A new type of feedforward neural network, PPLN (Projection Pursuit Learning NetWork), is introduced into the context of control system modeling. The mathematical principle and the learning strategy of PPLN are presented, and its characteristics is analyzed. PPLN is suitable for solving high dimension problems in that it is parsimonious and adaptive to small training data set. Simulation with CSTR show that the dynamic system is successfully identified. PPLN is proved have exciting advantages for modeling of mulli-variable control systems
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