Abstract: The variable step-size least mean square method for estimation in adaptive networks uses a variable step-size to provide estimation for each node in the adaptive network, where the step-size at each node is determined by the error calculated for each node, as opposed to conventional least mean square algorithms used in adaptive filters and the like, where the choice of step-size reflects a tradeoff between misadjustment and the speed of adaptation.

Abstract: The system and method for least mean fourth adaptive filtering is a system that uses a general purpose computer or a digital circuit (such as an ASIC, a field-programmable gate array, or a digital signal processor that is programmed to utilize a normalized least mean fourth algorithm. The normalization is performed by dividing a weight vector update term by the fourth power of the norm of the regressor.

Abstract: The variable step-size least mean square method for estimation in adaptive networks uses a variable step-size to provide estimation for each node in the adaptive network, where the step-size at each node is determined by the error calculated for each node, as opposed to conventional least mean square algorithms used in adaptive filters and the like, where the choice of step-size reflects a tradeoff between misadjustment and the speed of adaptation.

Abstract: The system and method for least mean fourth adaptive filtering is a system that uses a general purpose computer or a digital circuit (such as an ASIC, a field-programmable gate array, or a digital signal processor that is programmed to utilize a normalized least mean fourth algorithm. The normalization is performed by dividing a weight vector update term by the fourth power of the norm of the regressor.

Abstract: The noise-constrained diffusion least mean square method for estimation in adaptive networks is based on the Least Mean Squares (LMS) algorithm. The method uses a variable step size in which the step-size variation rule results directly from the noise constraint.

Abstract: The apparatus and method for blind block recursive estimation in adaptive networks, such as a wireless sensor networks, uses recursive algorithms based on Cholesky factorization (Cholesky) or singular value decomposition (SVD). The algorithms are used to estimate an unknown vector of interest (such as temperature, sound, pressure, motion, pollution, etc.) using cooperation between neighboring sensor nodes in the wireless sensor network. The method incorporates the Cholesky and SVD algorithms into the wireless sensor networks by creating new recursive diffusion-based algorithms, specifically Diffusion Blind Block Recursive Cholesky (DBBRC) and Diffusion Blind Block Recursive SVD (DBBRS). Both DBBRC and DBBRS perform much better than the no cooperation case where the individual sensor nodes do not cooperate. A choice of DBBRC or DBBRS represents a tradeoff between computational complexity and performance.

Abstract: The time-varying least-mean-fourth-based channel equalization method is an automated procedure that provides an adaptive equalizer in a CDMA receiver. Equalizer filter coefficients are estimated using a least-mean-fourth (LMF) error calculation based on a training set of symbols sent by the transmitter. When the LMF error calculation is combined with a power-of-two quantization (PTQ) process, superior receiver performance is achieved in a time-varying CDMA channel operating in non-Gaussian noise environments.

Abstract: The noise-constrained diffusion least mean square method for estimation in adaptive networks is based on the Least Mean Squares (LMS) algorithm. The method uses a variable step size in which the step-size variation rule results directly from the noise constraint.

Abstract: The variable step-size least mean square method for estimation in adaptive networks uses a variable step-size to provide estimation for each node in the adaptive network, where the step-size at each node is determined by the error calculated for each node, as opposed to conventional least mean square algorithms used in adaptive filters and the like, where the choice of step-size reflects a tradeoff between misadjustment and the speed of adaptation.

Abstract: The variable step-size least mean square method for estimation in adaptive networks uses a variable step-size to provide estimation for each node in the adaptive network, where the step-size at each node is determined by the error calculated for each node, as opposed to conventional least mean square algorithms used in adaptive filters and the like, where the choice of step-size reflects a tradeoff between misadjustment and the speed of adaptation.