A novel recurrent neural network with minimal representation for dynamic system identification

This paper presents a self-adaptive learning algorithm for dynamic system identification using a novel recurrent neural network with minimal representation. The proposed algorithm consists of two mechanisms, a minimal realization technique based on Markov parameters and a recursive parameter learning method on the ordered derivatives, for the minimal order identification and parameter optimization, respectively. Computer simulations on unknown dynamic system identification using the proposed approach have successfully validated: 1) the order of the recurrent network representation is minimal, and 2) the proposed network is able to closely capture the dynamical behavior of the unknown system with a satisfactory performance.