The theory of a posteriori error estimates suitable for adaptive refinement is well established. This work focuses on the fundamental, but less studied, issue of convergence rates of adaptive algorithms. In particular, this work describes a simple and general adaptive algorithm applied to ordinary, stochastic and partial differential equations with proven convergence rates. The presentation has...

The adaptive algorithm has been widely used in the digital signal processing like channel estimation, channel equalization, echo cancellation, and so on. One of the most important adaptive algorithms is the RLS algorithm. We present in this paper n multiple objective optimization approach to fast blind channel equalization. By investigating first the performance (mean-square error) of the stand...

The adaptive algorithm has been widely used in the digital signal processing like channel estimation, channel equalization, echo cancellation, and so on. One of the most important adaptive algorithms is the LMS algorithm. We present in this paper an multiple objective optimization approach to fast blind channel equalization. By investigating first the performance (mean-square error) of the stan...

We present new residual estimates based on Kato’s square root theorem for spectral approximations of non-self-adjoint differential operators of convection–diffusion–reaction type. These estimates are incorporated as part of an hp-adaptive finite element algorithm for practical spectral computations, where it is shown that the resulting a posteriori error estimates are reliable. Provided experim...

In this paper based on the conception of evolution, the Genetic Algorithm (GA) is applied to parameters optimization of proportional-integral-derivative (PID) controller for continuous stirred tank reactor (CSTR).The mathematical model of experimental system had been approximated near the operating point for the GA algorithm to adjust PID parameter for the minimum integral of time multiplied by...

Various Adaptive filtering methods are using to filter Cardiac signals. The two basic adaptive filtering algorithms are LMS (Least Mean Square) and RLS (Recursive Least Square). These Adaptive Algorithms are used to filter artifacts from ECG signal. Adaptive filter minimises the mean square error between the primary input, which is ECG with noise and desired response, which is either ECG or noi...

This paper presents a data-driven adaptive steady state-integral-derivative (SS-ID) control algorithm that uses gradient descent and recursive least squares (RLS) with forgetting factor. A simplified first-order differential equation of the system was designed its parameters were estimated in real-time using RLS algorithm. The steady-state input for target-state tracking derived based on perfor...

this study proposes a combination of a fuzzy sliding mode controller (fsmc) with integral-proportion-derivative switching surface based superconducting magnetic energy storage (smes) and pid tuned by a multi-objective optimization algorithm to solve the load frequency control in power systems. the goal of design is to improve the dynamic response of power systems after load demand changes. in t...

Regression models are used in many areas of signal processing, e.g., spectral analysis and speech LPC, where block processing methods have typically been used to estimate the unknown coefficients. Iterative methods for adaptive estimation fall into two categories: the least-mean-square (LMS) algorithm and the recursive-least-squares (RLS) algorithm. The LMS algorithm offers low complexity and s...

here a posteriori error estimate for the numerical solution of nonlinear voltena- hammerstein equations is given. we present an error upper bound for nonlinear voltena-hammastein integral equations, in which the form of nonlinearity is algebraic and develop a posteriori error estimate for the recently proposed method of brunner for these problems (the implicitly linear collocation method).we al...