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In order to deal with the problem in which the conventional Kalman filtering may be instable or divergent when noise statistics is unknown, a new adaptive filtering is presented, which is defined as memory-attenuated least square filtering (MALSF). The error covariance is multiplied by a decay factor to avoid the divergence and an adaptive estimation for decay factor is developed, and a recursive...
In this paper, we propose a simple but powerful neural decision-feedback equalizer trained with a fast-converging adaptive filter algorithm, which is efficient, simple, and numerically robust. The equalizer can be viewed as a neuron with fixed or adaptive sigmoidal nonlinearity. The simulation results on various time-invariant and time-varying channel equalization benchmarks have shown its surprisingly...
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