An MDL-based Hammerstein recurrent neural network for control applications

作者：Wang Jeen Shing^*; Hsu Yu Liang

来源：Neurocomputing, 2010, 74(1-3): 315-327.

DOI：10.1016/j.neucom.2010.03.011

• Hammerstein model
• Recurrent neural networks
• Minimum description length
• Nonlinearity eliminator

This paper presents an efficient control scheme using a Hammerstein recurrent neural network (HRNN) based on the minimum description length (MDL) principle for controlling nonlinear dynamic systems In the proposed control approach an unknown system 1, first identified by the MDL-based HRNN which consists of a static nonlinear model cascaded by a dynamic linear model and can be expressed in a state-space representation For high-accuracy system modeling we have developed a self construction algorithm that Integrates the MDL principle and recursive recurrent learning algorithm for constructing a parsimonious HRNN in an efficient manner To ease the control of the system we have established a nonlinearity eliminator that functions as the inverse of the static nonlinear model to remove the global nonlinear behavior of the unknown system If the system modeling and the Inverse of the nonlinear model are accurate the compound model the unknown system cascaded with the nonlinearity eliminator will behave like the linear dynamic model This assumption turns the task of complex nonlinear control problems into a simple feedback linear controller design Hence well-developed linear controller design theories can be applied directly to achieve satisfactory control performance Computer simulations on unknown nonlinear system control

• 出版日期2010-12