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A new prediction error identification method is proposed to identify multi-input-multi-output (MIMO) processes and the initial state. It linearizes the model output with respect to the model parameters so that optimal model parameters can be obtained in an analytic way. And, it has potential to provide a faster convergence rate and a better local minimum in solving the nonlinear optimization problem.
A new supervisory training rule for the multilayered feedforward neural network (FNN) using local linearization and analytic optimal solution is proposed. The cause of the nonlinearity of the neural network in the training is pinpointed and the nonlinearity is removed by a local linearization. And, the optimal solution of the linearized FNN minimizing the objective function for the training is analytically...
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