Dynamic modelling using neural networks

A neural network structure is presented that uses feedback of unmeasured system states to represent dynamic systems more efficiently than conventional feedforward and recurrent networks, leading to better predictions, reduced training requirement and more reliable extrapolation. The structure identifies the actual system states based on imperfect knowledge of the initial state, which is available in many practical systems, and is therefore applicable only to such systems. It also enables a natural integration of any available partial state-space model directly into the prediction scheme, to achieve further performance improvement. Simulation examples of three varied dynamic systems illustrate the various options and advantages offered by the state-feedback neural structure. Although the advantages of the proposed structure, compared with the conventional feedforward and recurrent networks, should hold for most practical dynamic systems, artificial systems can readily be created and real systems can surely be found for which one or more of these advantages would vanish or even get reversed. Caution is therefore recommended against interpreting the suggested advantages as strict theorems valid in all situations.