Digital IIR filter design using differential evolution algorithm

Any digital signal processing algorithm or processor can be reasonably described as a digital filter. The main advantage of an infinite impulse response (IIR) filter is that it can provide a much better performance than the finite impulse response (FIR) filter having the same number of coefficients. However, they might have a multimodal error surface. Differential evolution (DE) algorithm is a new heuristic approach mainly having three advantages; finding the true global minimum of a multimodal search space regardless of the initial parameter values, fast convergence, and using a few control parameters. In this work, DE algorithm has been applied to the design of digital IIR filters and its performance has been compared to that of a genetic algorithm

A new design method based on artificial bee colony algorithm for digital IIR filters

Digital filters can be broadly classified into two groups: recursive (infinite impulse response (IIR)) and non-recursive (finite impulse response (FIR)). An IIR filter can provide a much better performance than the FIR filter having the same number of coefficients. However, IIR filters might have a multi-modal error surface. Therefore, a reliable design method proposed for IIR filters must be based on a global search procedure. Artificial bee colony (ABC) algorithm has been recently introduced for global optimization. The ABC algorithm simulating the intelligent foraging behaviour of honey bee swarm is a simple, robust, and very flexible algorithm. In this work, a new method based on ABC algorithm for designing digital IIR filters is described and its performance is compared with that of a conventional optimization algorithm (LSQ-nonlin) and particle swarm optimization (PSO) algorithm. (c) 2009 The Franklin Institute. Published by Elsevier Ltd. All rights reserved

Designing digital IIR filters using ant colony optimisation algorithm

In order to transform and analyse signals that have been sampled from analogue sources, digital signal processing (DSP) algorithms are employed. The advantages of DSP are based on the fact that the performance of the applied algorithm is always predictable. There is no dependence on the tolerances of electrical components as in analogue systems. DSP algorithms can be reasonably described as a digital filter. Digital filters can be broadly divided into two-sub classes: finite impulse-response filters and infinite impulse-response (IIR) filters. Because the error surface of IIR filters is generally multi-modal, global optimisation techniques are required in order to avoid local minima and design efficient digital IIR filters. In this work, a new method based on the ant colony optimisation algorithm with global optimisation ability is proposed for digital IIR filter design. Simulation results show that the proposed approach is accurate and has a fast convergence rate, and the results obtained demonstrate that the proposed method can be efficiently used for digital IIR filter design. (C) 2004 Elsevier Ltd. All rights reserved

A new method for adaptive IIR filter design based on tabu search algorithm

Adaptive digital filters have proven their worth in a wide ran e of applications such as channel equalisation, noise reduction, echo cancelling, and system identification. These filters can be broadly classified into two groups: finite impulse-response (FIR) and infinite impulse-response (IIR) filters. IIR filters have become the target of increasing interest because these filters can reduce the filter order significantly as compared to FIR filters. Tabu search is a heuristic optimisation algorithm which has been originally developed for combinatorial optimisation problems. It simulates the general rules of intelligent problem solving and has the ability of discovering the global minima in a multi-modal search space. In this work, a novel method based on tabu search is described for the design of adaptive IIR filters. (c) 2004 Elsevier GmbH. All rights reserved

A parallel tabu search algorithm for digital filter design

Purpose - The purpose of the paper is to present a novel design method for the optimal finite word length (FWL) finite impulse response (FIR) filters

Efficient design of fixed point digital FIR filters by using differential evolution algorithm

Differential Evolution (DE) algorithm is a new heuristic approach which has been proposed particulary for numeric optimization problems. It is a population based algorithm like genetic algorithms using the similar operators; crossover, mutation and selection. In this work, DE algorithm has been applied to the design of fixed point digital Finite Impuls Response (FIR) filters and its performance has been compared to that of Genetic Algorithm (GA) and Least Squares Algorithm (LSQ)

Blind Source Separation with Multi-Objective Optimization for Denoising

Blind Source Separation is an optimization method frequently used in statistical signal processing applications. There are many application areas such as ambient listening, denoising, signal detection, and so on. In this study, a new Strength Pareto Evolutionary Algorithm 2-based signal separation method is proposed, which combines Multi-Objective Optimization and Blind Source Separation algorithms. The proposed method has been tested for denoising, which is widely used in biomedical signal processing. That is, the Electrocardiogram (ECG) and White Gaussian Noise are mixed together with normally distributed random numbers and the original signals of the mixed signals are obtained again. To evaluate the performance of the proposed method and others (Multi-Objective Blind Source Separation and Independent Component Analysis), the Signal-to-Noise Ratio (SNR) of the ECG signal obtained from mixed signals has been measured. As a result of the simulation studies, it is seen that the performance of the proposed method is satisfactory.</jats:p

Performance comparison of genetic and differential evolution algorithms for digital FIR filter design

Differential Evolution (DE) algorithm is a new heuristic approach mainly having three advantages; finding the true global minimum of a multi modal search space regardless of the initial parameter values, fast convergence, and using a few control parameters. DE algorithm which has been proposed particulary for numeric optimization problems is a population based algorithm like genetic algorithms using the similar operators; crossover, mutation and selection. In this work, DE algorithm has been applied to the design of digital Finite Impulse Response filters and compared its performance to that of genetic algorithm

Investigation of the performance of the Kaiser-Hamming window in design of QMF bank

In recent years, significant improvements have been in fast digital filters and filter banks. QMF (Quadrature Mirror Filter) bank, known as an application of multi-speed filter banks are widely used sub-band coding, image compression, word coding, biomedical signal processing and communication systems. In this study; QMF bank design is realized via a windowing method with prototype FIR (Finite Impulse Response) filter design and based on cascade bounded Kaiser and Hamming windows for eliminating undesired oscillations in QMF bank design. It has been investigated how the performance changed of the filter degree in designing filter with different window functions and at the result it is shown that Kaiser-Hamming window with lower stopband attenuation could be used in QMF bank design