Adaptive Neural Network Control by Adaptive Interaction
نویسندگان
چکیده
In this paper, we propose an approach to adaptive neural network control by using a new adaptation algorithm. The algorithm is derived from the theory of adaptive interaction. The principle behind the adaptation algorithm is a simple but efficient methodology to perform gradient descent optimization in the parametric space. Unlike the approach based on the back-propagation algorithm, this approach will not require the plant to be converted to its neural network equivalent, a major obstacle in early approaches. By applying this adaptive algorithm, the same adaptation as the back-propagation algorithm is achieved without the need of backward propagating the error throughout a feedback network. This important property makes it possible to adapt the neural network controller directly. Control of various systems, including non-minimum phase systems, is simulated to demonstrate the effectiveness of the algorithm.
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