Design Of Adaptive IIR Filters Research Articles

Adaptive iir filter design using self-adaptive search equation based artificial beecolony algorithm

Infinite impulse response (IIR) system identification problem is defined as an IIR filter modeling to represent an unknown system. During a modeling task, unknown system parameters are estimated by metaheuristic algorithms through the IIR filter. This work deals with the self-adaptive search-equation-based artificial bee colony (SSEABC) algorithm that is adapted to optimal IIR filter design. SSEABC algorithm is a recent and improved variant of artificial bee colony (ABC) algorithm in which appropriate search equation is determined with a self-adaptive strategy. Moreover, the success of the SSEABC algorithm enhanced with a competitive local search selection strategy was proved on benchmark functions in our previous studies. The SSEABC algorithm is utilized in filter modelings which have different cases. In order to demonstrate the performance of the SSEABC algorithm on IIR filter design, we have also used canonical ABC, modified ABC (MABC), best neighbor-guided ABC, and an ABC with an adaptive population size (APABC) algorithms as well as other algorithms in the literature for comparison. The obtained results and the analysis on performance evolution of compared algorithms on several filter design cases indicate that SSEABC outperforms all considered ABC variants and other algorithms in the literature.

A modified adaptive IIR filter design via wavelet networks based on Lyapunov stability theory

In this paper, we present a wavelet network IIR filtering system satisfying asymptotic stability in the sense of Lyapunov unlike many other gradient descent algorithms based adaptive filtering systems. The proposed system also carries the advantages of the time-frequency specific properties of wavelet networks embedded into the proposed filter dynamics. Two experiments for system identification problems corresponding to the infinite impulse response filter design are proposed. The results verified that the proposed wavelet network infinite impulse response adaptive filtering system not only performs better than gradient descent based algorithms but also performs as good as other stability theory based optimization algorithms.

Adaptive IIR filter design for single sensor applications

The goal of this research was to investigate the theoretical design and physical implementation of a digital adaptive IIR filter to serve as an enhancement to the traditional active RC or passive RLC anti-aliasing filter. This all-digital filter will reside directly on the DSP engine. As explained in the paper, the adaptive IIR filter is designed to process an oversampled signal coming from a single sensor to reject noise in an acquisition system. Differentiation between the noise and the signal is obtained by exploiting the different auto-correlation functions of the two signals. In contrast to oversampling techniques employed in sampled data systems that are designed to relax the requirements of an analog anti-aliasing filter, this filter will track a signal in the frequency domain. Several power spectral density plots are given to illustrate the performance of the new filter. The results also indicate that the new filter performs well as compared to the Wiener filter in the stationary case.

Continuous action reinforcement learning automata and their application to adaptive digital filter design

In the design of adaptive IIR filters, the multi-modal nature of the error surfaces can limit the use of gradient-based and other iterative search methods. Stochastic learning automata have previously been shown to have global optimisation properties making them suitable for the optimisation of filter coefficients. Continuous action reinforcement learning automata are presented as an extension to the standard automata which operate over discrete parameter sets. Global convergence is claimed, and demonstrations are carried out via a number of computer simulations.

Recursive LMS L-filters for noise removal in images

The problem of designing the weights for recursive L-filters optimized by the least mean square (LMS) algorithm is addressed. The coefficients derived for nonrecursive filtering are not optimal for recursive implementation, where the estimate of current pixel depends on the past outputs of the filter. To combat this, analogous to the design of adaptive IIR filters, the optimization scheme referred to as equation-error formulation is employed. The recursive filter performs better in suppressing noise than its nonrecursive counterpart.

System identification using discrete orthogonal functions

AbstractIn this paper a novel method is described for the design of adaptive IIR filters used in system identification. the adaptive filter is implemented as a parallel connection of subsections whose transfer functions constitute a set of discrete orthogonal systems. the adaptation algorithm used, which is of the Gauss‐Newton type, adjusts the parameters of these discrete orthogonal systems in order to match the desired output data of the unknown plant in a least squares sense. Owing to the orthogonality property, which ensures complete independence of subsequent sections, convergence is very rapid. Closed‐form expressions for the gradient signals required to update the filter are given. Illustrative examples have shown that this method always results in much improved adaptation properties compared with the already existing approaches.