Abstract: We consider the problem of adaptive filtering and related derivative estimating for signals with uncertain dynamics this paper.

This work aims to deal with performance analysis and enhancements for the adaptive algorithms and their applications. We first develop a new variable step-size ...

We consider the problem of adaptive filtering and related derivative estimating for signals with uncertain dynamics this paper. Based on the control method of ...

Jan 10, 2024 · In this paper a practical algorithm is presented for adaptive state filtering when the underlying nonlinear state equations are partially known.

May 24, 2024 · Kernel adaptive filtering (KAF) and fractional derivative are effective methods for dealing with limited data sample sizes and non ...

In this work, we seek to improve upon hand-derived adaptive filter algorithms and present a comprehensive framework for learning online, adaptive signal.

Regular algorithms of adaptive estimation of a condition of objects of management with uncertainty are given in models of dynamics and external indignations ...

Jan 2, 2020 · This paper is concerned with the design of a state filter for a time-delay state-space system with unknown parameters from noisy observation ...

... Filtering. Doctoral Thesis to obtain the academic ... It is shown that this algorithm outperforms competing algorithms significantly in many scenarios.

The update scheme for the proportionate adaptive algorithm is generalized as follows: ... Umeda, “An adaptive filtering algorithm using an orthogonal.