It is well known that the transient behaviors of the traditional adaptive control may be very poor in general, and that the adaptive control designed based on switching between multiple models is an intuitively appealing and practically feasible approach to improve the transient performances. This paper proves that for a typical class of linear systems disturbed by white noises, the multiple mo...

A new kernel adaptive filtering (KAF) algorithm, namely the sparse kernel recursive least squares (SKRLS), is derived by adding a l1-norm penalty on the center coefficients to the least squares (LS) cost (i.e. the sum of the squared errors). In each iteration, the center coefficients are updated by a fixed-point sub-iteration. Compared with the original KRLS algorithm, the proposed algorithm ca...

In this paper, estimation and identification theories will be examined with the goal of determining some new methods of adding robustness. The focus will be upon uncertain estimation problems, namely ones in which the uncertainty multiplies the quantities to be estimated. Mathematically the problem can be stated as, for system matrices and data matrices that lie in the sets (A + δA) and (b + δb...

The following is a brief review of least squares optimization and constrained optimization techniques, which are widely used to analyze and visualize data. Least squares (LS) optimization problems are those in which the objective (error) function is a quadratic function of the parameter(s) being optimized. The solutions to such problems may be computed analytically using the tools of linear alg...

Supervised learning problem with Universum data is a new research subject in machine learning. Universum data, which are not belonging to any class of the classification problem of interest, has been proved very helpful in learning. For data classification with Universum data, a novel quick classifier is proposed in this paper and named as least squares Universum twin support vector machine (LS...

In this letter, we comment on "Pruning Error Minimization in Least Squares Support Vector Machines" by B. J. de Kruif and T. J. A. de Vries. The original paper proposes a way of pruning training examples for least squares support vector machines (LS SVM) using no regularization (-gamma = infinity). This causes a problem as the derivation involves inverting a matrix that is often singular. We di...

In the forward link of WCDMA systems, the multipath propagation channel destroys the orthogonality of the spreading codes and therefore causes multi-user interference (MUI). In this paper, we propose new training-based and semi-blind space-time chip equalizer receivers for the forward link of WCDMA systems with a continuous code-multiplexed pilot. Both Least-Squares (LS) algorithms for block pr...

MIMO-OFDM system is a new wireless broadband technology which has gained great popularity for its capability of high rate transmission and its robustness against multi-path fading and other channel impairments. Precise and competent channel estimation for MIMO-OFDM systems is necessary to improve error performance of the system. In this paper, Least Squares (LS) channel estimation is implemente...