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...

Supply voltage compensation is important to keep the accuracy for hot wire/hot film gas flow sensors. A compensation experiment device is established, the two steps curve fitting is presented to determine the functional relations among flow、sensor output and supply voltage, the coefficient of fitting polynomial is determined by Least Square method, thus eliminating the influence of supply volta...

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...

Ensemble of networks has been proven to give better prediction result than a single network. Two commonly used way of determining the ensemble weights are simple average ensemble method and the generalized ensemble method. In the paper, we propose the weighted least square ensemble network. The major difference between this method and the other ensemble methods is that we do not assume that nei...

In this paper, we have given an idea of area specification and its corresponding sensing of nodes in a dynamic network. We have applied the concept of Monte Carlo methods in this respect. We have cited certain statistical as well as artificial intelligence based techniques for realizing the position of a node. We have also applied curve fitting concept for node detection and relative verificati...

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...

Given a dense set of points lying on or near an embedded submanifold M0 ⊂ Rn of Euclidean space, the manifold fitting problem is to find an embedding F :M → Rn that approximatesM0 in the sense of least squares. When the dataset is modeled by a probability distribution, the fitting problem reduces to that of finding an embedding that minimizes Ed[F], the expected square of the distance from a po...