A study on interrelation between the structure of a Plant and the str neural network emulator and the learning rate

플랜트구조와 신경망에뮬레이터의 구조 및 학습시간과의 관계

Error-backpropagation has been used in the bulk of Practical applications for neural networks. While an emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. There is, however, no concrete theoretical results about the structure of a plant and the structure of a multilayered neural network and the learning rate. The paper investigates the relation between structure of a plant and a multilayered network and learning rate. Simulation study shows that the plant signal with a short period and a fast sam time is preferable for learning of the network emulator.