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A learning algorithm for applying synthesized stable dynamics to system identification
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DOI：10.1016/S0893-6080(97)00109-3.png)
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In this paper the models discussed by Cohen are extended by introducing an input term. This allows the resulting models to be utilized for system identification tasks. This approach gives a direct way to encode qualitative information such as attractor dimension into the model. We prove that this model is stable in the sense that a bounded input leads to a bounded state when a minor restriction is imposed on the Lyapunov function. By employing this stability result, we are able to find a learning algorithm which guarantees convergence to a set of parameters for which the error between the model trajectories and the desired trajectories vanishes. (C) 1998 Elsevier Science Ltd. All lights reserved.
Keyword:
stability
learning algorithm
system identification
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