Aydin, G.Arıkan, OrhanÇetin, A. Enis2016-02-082016-02-081999-021057-7130http://hdl.handle.net/11693/25271A new class of algorithms based on the fractional lower order statistics is proposed for finite-impulse response adaptive filtering in the presence of α-stable processes. It is shown that the normalized least mean p-norm (NLMP) and Douglas' family of normalized least mean square algorithms are special cases of the proposed class of algorithms. A convergence proof for the new algorithm is given by showing that it performs a descent-type update of the NLMP cost function. Simulation studies indicate that the proposed algorithms provide superior performance in impulsive noise environments compared to the existing approaches.EnglishAdaptive filteringFIR filtersMathematical modelsProbability density functionRandom processesSpurious signal noiseStabilityStatistical methodsAlpha stable random processesFinite impulse response adaptive filteringImpulsive signalsLeast mean square algorithmsNormalized least mean p normAdaptive algorithmsArticle10.1109/82.752953