| 摘要 |
<p>The invention relates to a method and a neurone layer structure for the neural modelling of dynamic systems. To this end, parameters describing inertia and parameters describing acceleration of the system's time series are trained and processed separately in the network. The prognostic values thus obtained are combined to give a desired prognostic quantity. Different target quantities in the form of average values with bases of different widths can be obtained by defining different indicators for each dynamic parameter. A greater fault current for returning to the network is generated by training these values. This makes possible exact simulation of the different dynamic parameters. The inventive structure and method are preferably used for stock exchange forecasts and for other dynamic systems.</p> |
