The comparison of the performance in the identification between SBP and DBP for a plant with output noise

출력잡음을 가진 플랜트에 대한 SBP 와 DBP의 식별성능 비교

This paper introduces an identification model called the Dynamic Neural Network(DNN) with a multilayer neural network in the forward path and a linear dynamical system in the feedback path, and defines Dynamic BackPropagation(DBP) as a learning algorithm for it. This identification model uses the feedback of its own output as a learning signal, which is not affected by a noise added to the output terminal of the plant so, it can be considered as a parallel identification model, and when compared with a series-parallel model which does not use the concept of the feedback, the proposed identification scheme exhibits more robust performance.