Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
System identification based on bounded error constraints
IEEE Transactions on Signal Processing
On the value of information in system identification-Bounded noise case
Automatica (Journal of IFAC)
Paper: Adaptive systems, lack of persistency of excitation and bursting phenomena
Automatica (Journal of IFAC)
Using recurrent neural networks for adaptive communication channel equalization
IEEE Transactions on Neural Networks
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Gradient-type adaptive channel equalizers can suffer from burst error problems under sustained fading and in the absence of sufficient excitation. To overcome this problem, we propose an algorithm called the gradient-with-projection-on-bounding-ellipsoid (GPrOBE). The main idea in the proposed GPrOBE algorithm is that the equalizer parameters are computed by projecting the gradient estimate onto a set that is updated using a priori information on the instantaneous error magnitude. Simulation results show an improvement in the performance of the GPrOBE algorithm over other gradient-based algorithms in terms of reduced error bursts. Normalized error energy (NEE) performance analysis highlights that the GPrOBE algorithm yields solutions that are close to minimum mean-square-error (MMSE), while providing instantaneous performance guarantees during periods of insufficient excitation. Bit error rate (BER) experiments reveal that the GPrOBE algorithm is more robust in channels with sustained fading.