Abstract
In this article, a time-varying two-phase optimization neural network is proposed for the constrained time-varying optimization problem, which takes advantage of both the two-phase neural network and the time-varying programming neural network. Considering the training of a neural network as a time-varying optimization problem, the proposed algorithm is applied to the multilayer neural network training for the system identification or function learning and the model reference neurocontrol. Moreover, the neural network training with the constrained weights is also considered. The effectiveness of the proposed scheme is demonstrated by computer simulations.
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