The diffusion least mean square (DLMS) and the normalized (DNLMS) algorithms are analyzed for a network having fusion center. This structure reduces dimensionality of resulting stochastic models while preserving important properties. analysis is done in system identification framework cyclostationary white nodal inputs. parameters vary according to random walk model. cyclostationarity modeled b...

in this study, the substrate diffusion in an immobilized spherical cell-support aggregate is studied and effects of various parameters are investigated on substrates profile. analyses are performed by using of an analytical solution called the least square method (lsm) and results are compared with numerical solution. the effects of effective diffusion coefficient ( d e ), maximum specific grow...

Learning with Fredholm kernel has attracted increasing attention recently since it can effectively utilize the data information to improve the prediction performance. Despite rapid progress on theoretical and experimental evaluations, its generalization analysis has not been explored in learning theory literature. In this paper, we establish the generalization bound of least square regularized ...

As one of the recently proposed algorithms for sparse system identification, l0 norm constraint Least Mean Square (l0-LMS) algorithm modifies the cost function of the traditional method with a penalty of tap-weight sparsity. The performance of l0-LMS is quite attractive compared with its various precursors. However, there has been no detailed study of its performance. This paper presents compre...

In Part I of this work, the moving least-square reproducing kernel (MLSRK) method is formulated and implemented. Based on its generic construction, an m-consistency structure is discovered and the convergence theorems are established. In this par\ of the work, a systematic Fourier analysis is employed to evaluate and further establish the method. The preliminary Fourier analysis reveals that th...

The aim of this work is to show how partial least squares (PLS) regression when combined with two other techniques Karhunen-Loeve (KL) expansion and Markov chain Monte Carlo (MCMC) can be efficient and effective at addressing parameter uncertainties that affect the predictive ability of a model for critical applications such as monitoring and control. We introduce a combination of PLS regressio...