한국정밀공학회지 (Journal of the Korean Society for Precision Engineering)
- 제12권6호
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- Pages.120-127
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- 1995
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- 1225-9071(pISSN)
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- 2287-8769(eISSN)
지역시간지연 순환형 신경회로망을 이용한 비선형 시스템 규명
System Identification of Nonlinear System using Local Time Delayed Recurrent Neural Network
초록
A nonlinear empirical state-space model of the Artificial Neural Network(ANN) has been developed. The nonlinear model structure incorporates characteristic, so as to enable identification of the transient response, as well as the steady-state response of a dynamic system. A hybrid feedfoward/feedback neural network, namely a Local Time Delayed Recurrent Multi-layer Perception(RMLP), is the model structure developed in this paper. RMLP is used to identify nonlinear dynamic system in an input/output sense. The feedfoward protion of the network architecture provides with the well-known curve fitting factor, while local recurrent and cross-talk connections provides the dynamics of the system. A dynamic learning algorithm is used to train the proposed network in a supervised manner. The derived dynamic learning algorithm exhibit a computationally desirable characteristic; both network sweep involved in the algorithm are performed forward, enhancing its parallel implementation. RMLP state-space and its associate learning algorithm is demonstrated through a simple examples. The simulation results are very encouraging.
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