A simple, yet powerful, learning method is presented by combining the famed kernel trick and the least-mean-square (LMS) algorithm, called the KLMS. General properties of the KLMS algorithm are demonstrated regarding its well-posedness in very high dimensional spaces using Tikhonov regularization theory. An experiment is studied to support our conclusion that the KLMS algorithm can be readily u...

In this technical report we analyse the performance of diffusion strategies applied to the Least-Mean-Square adaptive filter. We configure a network of cooperative agents running adaptive filters and discuss their behaviour when compared with a non-cooperative agent which represents the average of the network. The analysis provides conditions under which diversity in the filter parameters is be...

In this work, a new class of stochastic gradient algorithm is developed based on q-calculus. Unlike the existing q-LMS algorithm, the proposed approach fully utilizes the concept of q-calculus by incorporating time-varying q parameter. The proposed enhanced q-LMS (Eq-LMS) algorithm utilizes a novel, parameterless concept of error-correlation energy and normalization of signal to ensure high con...

one of the most valuable and important application of artificial satellites in geodetic sciences is recovery of the earth's gravity field. orbits of satellites either gravimetric or nongravimetric are analyzed to improve the earth's gravity field. gravimetric satellites are launched at low altitudes to observe gravity field in more detail. grace twin satellites are the second spacecra...

In this paper we show that the weighted least square (WLS) and the logarithmic least square (LLS) of various methods to solve the mutual evaluation network (MEN) system including the Analytic Hierarchy Process (AHP) and the Analytic Network Process (ANP), are superior to others. Both methods can solve MEN problems without any restrictions of the structure matrix of MEN. We also show that the WL...