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.
|主出版物標題||2004 IEEE International Joint Conference on Neural Networks - Proceedings|
|出版狀態||Published - 2004|
|事件||2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary|
持續時間: 2004 七月 25 → 2004 七月 29
|Other||2004 IEEE International Joint Conference on Neural Networks - Proceedings|
|期間||04-07-25 → 04-07-29|
All Science Journal Classification (ASJC) codes