Ai Hui Tan

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Direction-dependent dynamic systems are defined, and Wiener models for them are described. For first-order systems with pseudo-random binary inputs, optimising the model parameters by cross-correlation function matching methods based on analysis gives excellent results. For first-order systems with inverse-repeat pseudo-random binary inputs, optimisation by(More)
—The modeling of direction-dependent dynamic processes using Wiener models and recurrent neural network models with nonlinear output error structure is considered. The results obtained are compared for several simulated first-order and second-order processes and using three different types of input signals: a pseudorandom binary signal, an inverse-repeat(More)
This paper proposes a new mixed training algorithm consisting of error backpropagation (EBP) and variable structure systems (VSSs) to optimize parameter updating of neural networks. For the optimization of the number of neurons in the hidden layer, a new term based on the output of the hidden layer is added to the cost function as a penalty term to make(More)