Time-delay Neural Networks for Control
نویسنده
چکیده
This paper presents results regarding the application of Time-Delay Neural Networks (TDNNs), up to now mainly used in speech recognition, for control tasks. A set of examples taken from a model-based robot controller is used to validate the suitability of the TDNN and to show its superiority to standard multilayer perceptrons. Afterwards, a new algorithm is presented that shows how the inherent capability of the TDNN to deal with time-variant signals can be employed to directly generate a TDNN from a PID control law. This synthesized neural controller is operational from the very beginning and can be tuned on-line using existing learning techniques. The advantages of this design procedure are shown for a simple control task.
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