Modeling Dynamic System by Recurrent Neural Network with State Variables

Abstract

A study is performed to investigate the state evolution of a kind of recurrent neural network. The state variable in the neural system summarize the information of external excitation and initial state, and determine its future response. The recurrent neural network is trained by the data from a dynamic system so that it can behave like the dynamic system. The dynamic systems include both input-output black-box system and autonomous chaotic system. It is found that the state variables in neural system differ from the state variable in the black-box system identified, this case often appears when the network is trained with input-output data of the system. The recurrent neural system learning from chaotic system exhibits an expected chaotic character, its state variable is the same as the system identified at the first period of evolution and its state evolution is sensitive to its initial state.