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In this paper, a new control mechanism for the variable forgetting factor (VFF) of the recursive least square (RLS) adaptive algorithm is presented. The control algorithm is basically a gradient-based method of which the gradient is derived from an improved mean square error analysis of RLS. The new mean square error analysis exploits the correlation of the(More)
In impulsive noise environment, most learning algorithms are encountered difficulty in distinguishing the nature of large error signal, whether caused by the impulse noise or model error. Consequently, they suffer from large misadjustment or otherwise slow convergence. A new nonlinear RLS (VFF-NRLS) adaptive algorithm with variable forgetting factor for FIR(More)
The race for the next generation of painless and reliable glucose monitoring for diabetes mellitus is on. As technology advances, both diagnostic techniques and equipment improve. This review describes the main technologies currently being explored for noninvasive glucose monitoring. The principle of each technology is mentioned; its advantages and(More)
Partial least squares discriminant analysis (PLS-DA) is widely used in multivariate calibration method. Very often, only one single quantitative model is constructed to predict the relationship between the response and the independent variables. This approach can easily misidentify, under or over estimate the important features contained in the independent(More)
Background Diabetes mellitus is prevalent worldwide and the majority of the patients with this metabolic disease are managed in primary healthcare settings. Self-management is, therefore, crucial for the health and wellbeing of people with diabetes. Due to the advancement of information technologies, telehealth intervention as self-management measures(More)
Absrracr This paper is to present the performance analysis of recursive least square algorithm with errorsaturation in mixture noise. The algorithm is referred to as nonlinear RLS (NRLS). Generalized clipping function is considered for the error-saturation nonlinearity. An improved mean square behavior of NRLS is carried out. It is shown that the(More)
In environment with impulsive noise, most learning algorithms are encountered difficulty in distinguishing the nature of large error signal, whether caused by the impulse noise or large model error. Consequently, they suffer from slow convergence or large misadjustment. A new gradient based variable forgetting factor nonlinear RLS algorithm uses correlation(More)
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