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By utilizing QR decomposition technique, an orthogonal iterative learning least squares algorithm is proposed for time-varying high-order neural network training, which is applied for the identification of time-varying nonlinear systems over a finite time interval. With the help of two-dimensional Givens transformation, both on-line and off-line identification procedures are presented for weights...
This paper presents a neural network framework for implementing unknown time-varying mappings. A unified architecture of time-varying neural networks is proposed, and the methodology of iterative learning is used for the network training. Convergence results of the iterative learning least squares algorithm are derived under assumption of bounded input signals. Periodic neural networks are explored...
In this paper, periodic learning control is presented for deterministic periodic auto-regressive exogenous systems. The control problem is approached in a certainty equivalence framework, of which a periodic learning identification algorithm is formed to estimate the periodic time-varying parameters, and the only prior knowledge is the periodicity. The learning algorithm updates the estimates periodically,...
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