Dynamic Relative Gain Array Estimation using Local Polynomial Approximation Approach

Linearity Testing Using Local Polynomial Approximation

Reachability-Based Approach for L2 Gain Approximation of Polynomial Systems

This paper considers a computational mechanism to approximate a class of polynomial systems with a reduced order model. The approach is based on estimate of the reachability set and a finite gain L2 stability condition. The approach benefits from the use of sum of squares programming where the computation is rendered tractable.

THE COMPARISON OF TWO METHOD NONPARAMETRIC APPROACH ON SMALL AREA ESTIMATION (CASE: APPROACH WITH KERNEL METHODS AND LOCAL POLYNOMIAL REGRESSION)

Small Area estimation is a technique used to estimate parameters of subpopulations with small sample sizes.  Small area estimation is needed  in obtaining information on a small area, such as sub-district or village.  Generally, in some cases, small area estimation uses parametric modeling.  But in fact, a lot of models have no linear relationship between the small area average and the covariat...

Dynamic time-series forecasting using local approximation

Pattern recognition techniques for time-series forecasting are beginning to be realised as an important tool for predicting chaotic behaviour of dynamic systems. In this paper we develop the concept of a Pattern Modelling and Recognition System which is used for predicting future behaviour of time-series using local approximation. In this paper we compare this forecasting tool with neural netwo...

fast dynamic orbit determination of leo satellites using the legendre polynomial approximation

the low earth orbiting (leo) satellites are widely used for geosciences applications. for most applications, precise orbital information of the satellites is required. a combination of the in suite observations and dynamic orbit yields the optimum solution. in order to obtain the combined optimal solution, one needs to analytically or numerically propagate the state vector epoch by an epoch bas...