In this brief, a noise-free maximum correntropy criterion (NFMCC) algorithm is proposed for system identification in non-Gaussian environments. The utilizes theory to construct cost function which realized based on normalized Gaussian kernel. addition, new dynamic step size scheme enhance the performance of algorithm, implemented by minimizing posteriori error signal, and mean square deviation (MSD) greatly decreased. NFMCC shows significant property reducing detrimental effects outliers...

A soft parameter function penalized normalized maximum correntropy criterion (SPF-NMCC) algorithm is proposed for sparse system identification. The SPF-NMCC derived on the basis of adaptive filter theory, (MCC) and zero-attracting techniques. incorporated into cost traditional MCC (NMCC) to exploit sparsity properties signals. mathematically in detail. As a result, can provide an efficient zero attractor term effectively attract taps near-zero coefficients zero, and, hence, it speed up...

A sparsity-aware least-mean mixed-norm (LMMN) adaptive filter algorithm is proposed for sparse channel estimation applications. The realized by incorporating a sum-log function constraint into the cost of LMMN which mixed norm controlled scalar-mixing parameter. As result, shrinkage given to enhance performance when majority taps are zeros or near-zeros. behaviors reweighted investigated and discussed in comparison with those standard LMS square/fourth (LMS/F) previously LMS/F algorithms....

A general zero attraction (GZA) proportionate normalized maximum correntropy criterion (GZA-PNMCC) algorithm is devised and presented on the basis of proportionate-type adaptive filter techniques attracting theory to highly improve sparse system estimation behavior classical MCC within framework identifications. The newly-developed GZA-PNMCC carried out by introducing a parameter adjusting function into cost typical (PNMCC) create term. developed optimization unifies derivation...

A sparse set-membership normalised least mean square (SM-NLMS) algorithm with a correntropy penalty is proposed and its performance investigated for estimating echo channel. The SM-NLMS derived by minimising an unconstraint cost function that utilises the on weight vector as well sum of symmetric positive definite matrix constrained Euclidean norm differences between instantaneous error upper bound SM filtering. Simulation results over channel show superior to existing algorithms respect...

A group-constrained maximum correntropy criterion (GC-MCC) algorithm is proposed on the basis of compressive sensing (CS) concept and zero attracting (ZA) techniques its estimating behavior verified over sparse multi-path channels. The implemented by exerting different norm penalties two grouped channel coefficients to improve estimation performance in a mixed noise environment. As result, attraction term obtained from expected l 0 1 penalty techniques. Furthermore, reweighting factor...

Abstract In this paper, a triple stop-band filter with ratioed periodical defected microstrip structure is proposed for wireless communication applications. The structures are spiral slots, which embedded into 50

In the chiral unitary approach, we have studied single Cabibbo-suppressed decays $\Lambda_c\to pK^+K^-$ and $\Lambda_c \to p \pi^+\pi^-$ by taking into account $s$-wave meson-meson interaction as well contributions from intermediate vectors $\phi$ $\rho^0$. Our theoretical results for ratios of branching fractions \bar{K}^{*0}$ \omega$ with respect to one \phi$ are in agreement experimental data. Within picture that scalar resonances $f_0(500)$, $f_0(980)$, $a_0(980)$ dynamically generated...

In multi-label learning, leveraging contrastive learning to learn better representations faces a key challenge: selecting positive and negative samples effectively utilizing label information. Previous studies selected based on the overlap between labels used them for label-wise loss balancing. However, these methods suffer from complex selection process fail account varying importance of different labels. To address problems, we propose novel method that improves through distribution....

A robust sparse least-mean mixture-norm (LMMN) algorithm is proposed, and its performance appraised in the context of estimating a broadband multi-path wireless channel. The proposed implemented via integrating correntropy-induced metric (CIM) penalty into conventional LMMN to modify basic cost function, which denoted as CIM-based (CIM-LMMN) algorithm. CIM-LMMN derived detail within kernel framework. updating equation can provide zero attractor attract non-dominant channel coefficients...

In this paper, a p-norm-like constraint is utilized to develop sparse least mean fourth algorithm for channel estimation. By incorporating the into cost function of conventional (LMF) algorithm, (PNC-LMF) achieved exploit sparsity property broadband wireless communication channel. The proposed PNC-LMF aims seek tradeoff between effects and estimation errors, which also verified by simulation compared with LMF previously reported popular algorithms. simulated results show that has faster...

A low-complexity norm-adaption least-mean-square/fourth (LCNA-LMS/F) algorithm is proposed to exploit the sparse properties of wireless multi-path channel in this paper. The LCNA-LMS/F realized by using a segment function instead reweighting factor reweighted (RNA-LMF) remove division operation, which can reduce computational complexity. estimation behaviors are investigated over and computer simulation results show that achieves superior performance with respect convergence speed...

A norm combination penalized set-membership NLMS algorithm with<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mn mathvariant="normal">0</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>and<mml:math id="M2"><mml:mrow><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>independently constrained, which is denoted...

Sparse channel estimation has attracted more attention for various broadband wireless communication systems. Square error criterion based adaptive filter algorithms are extensively studied sparse estimations (SCE) such as zero-attracting (ZA) least mean square (ZA-LMS) and reweighting ZA-LMS (RZA-LMS) algorithms. However, these LMS usually sensitive to the scaling of their input signal. In this paper, an improved algorithm is proposed on basis normalized (NLMS) algorithm, reweighted ZA (RZA)...

An adaptive combination constrained proportionate normalized maximum correntropy criterion (ACC-PNMCC) algorithm is proposed for sparse multi-path channel estimation under mixed Gaussian noise environment. The developed ACC-PNMCC implemented by incorporating an function into the cost of (PNMCC) to create a new penalty on filter coefficients according devised threshold, which based proportionate-type techniques and compressive sensing (CS) concept. derivation mathematically presented, various...

A proportionate-type normalized maximum correntropy criterion (PNMCC) with a induced metric (CIM) zero attraction terms is presented, whose performance also discussed for identifying sparse systems. The proposed algorithms utilize the advantage of proportionate schemed adaptive filter, (MCC) algorithm, and theory. CIM scheme incorporated into basic MCC to further sparsity inherent systems, resulting in name CIM-PNMCC algorithm. derivation given. are used evaluating systems non-Gaussian...

In this paper, a correntropy induced metric (CIM) criterion based set-membership NLMS (SM-NLMS) algorithm is proposed and its derivation given in detail for estimating sparse channel identification system. the algorithm, CIM utilized to exploit sparsity-aware property of broadband multiple-path channels achieve better state information (CSI). Moreover, SM-NLMS (CIMSM-NLMS) carried out by mimicking modified cost function under restricted condition CIM. The estimation performance CIMSM-NLMS...