We develop a linearly-constrained line-search adaptive filtering algorithm by incorporating the linear constraints into the least squares problem and searching the solution (filter weights) along the Kalman gain vector. The proposed algorithm performs close to the constrained recursive least squares (CRLS) algorithm while having a computational complexity comparable to the constrained least mea...

Several extensions of the total least squares (TIS) method that are able to calculate a structured rank deficient approximation of a data matrix have been developed recently. The main result of this correspondence is the demonstration of the equivalence of two of these approaches, namely, the constrained total least squares (CTLS) approach and the structured total least squares (STLS) approach....

We present a methodology for characterization and an approach for computing the solutions of fuzzy linear systems with LR fuzzy variables. As solutions, notions of exact and approximate solutions are considered. We transform the fuzzy linear system into a corresponding linear crisp system and a constrained least squares problem. If the corresponding crisp system is incompatible, then the fuzzy ...

The two major obstacles toward high-capacity indoor wireless networks are distortion due to the indoor channel and the limited bandwidth which necessitates a high spectral efficiency. A combined orthogonal frequency division multiplexing (OFDM) / spatial division multiple access (SDMA) approach can efficiently tackle both obstacles and paves the way for cheap, high-capacity wireless indoor netw...

The linear least mean squares (LMS) algorithm has been recently extended to a reproducing kernel Hilbert space, resulting in an adaptive filter built from a weighted sum of kernel functions evaluated at each incoming data sample. With time, the size of the filter as well as the computation and memory requirements increase. In this paper, we shall propose a new efficient methodology for constrai...