Adaptive nonlinear equalizer with reduced computational complexity
Signal Processing
A fast new algorithm for training feedforward neural networks
IEEE Transactions on Signal Processing
Iterative reweighted least-squares design of FIR filters
IEEE Transactions on Signal Processing
A rapid supervised learning neural network for function interpolation and approximation
IEEE Transactions on Neural Networks
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Logistic models, comprising a linear filter followed by a nonlinear memoryless sigmoidal function, are often found in practice in many fields, e.g., biology, probability modelling, risk prediction, forecasting, signal processing, electronics and communications, etc., and in many situations a real time response is needed. The online algorithms used to update the filter coefficients usually rely on gradient descent (e.g., nonlinear counterparts of the Least Mean Squares algorithm). Other algorithms, such as Recursive Least Squares, although promising improved characteristics, cannot be directly used due to the nonlinearity in the model. We propose here a modified Recursive Least Squares algorithm that provides better performance than competing state of the art methods in an adaptive sigmoidal plant identification scenario.