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A new approach to designing sets of ternary periodic signals with different periods for multi-input multi-output system identification is described. The signals are pseudo-random signals with uniform nonzero harmonics, generated from Galois field , where is a prime or a power of a prime. The signals are designed to be uncorrelated, so that effects of(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)
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 secondorder processes and using three different types of input signals: a pseudorandom binary signal, an inverse-repeat(More)