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A study on the identification and control of dynamic system using neural networks

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KMID : 0613219970150000221
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Abstract

This paper is presented the identification and control of dynamic system using neural networks. The network has two input elements when modelling a single-output plant, one to receive the plant output and the other, an error input to compensate ofr modelling uncertainties. The network has feedback connections from its output, hidden, and input layers to its"state"layer and self-connections within the "state" layer.
The essential point of the proposed approach is to make use of the direct inverse learning scheme to achieve simple and robust inverse system identification. This approach can easily be extended to the area of on-line adaptive control which is briefly introduced. Simulation results are given to illustrate the usefulness of the method for the simpler case of controlling time-invariant plant.
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