IJETR2275.pdf

International Journal of Advanced Computer Science and Applications, 2010

The main focus of this paper is to describe a developed and dynamic method of designing finite impulse response filters with automatic, rapid and less computational complexity by an efficient Genetic approach. To obtain such efficiency, specific filter coefficient coding scheme has been studied and implemented. The algorithm generates a population of genomes that represents the filter coefficient where new genomes are generated by crossover, mutation operations methods. Our proposed genetic technique has able to give better result compare to other method.

2017

Genetic Algorithms (GAs) are used to solve many optimization problems in science and engineering such as pattern recognition, robotics, biology, medicine, and many other applications. The aim of this paper is to describe a method of designing Finite Impulse Response (FIR) filter using Genetic Algorithm (GA). Digital filters are an essential part of DSP. The purpose of the filters is to allow some frequencies to pass unaltered, while completely blocking others. The digital filters are mainly used for two purposes: separation of signals that have been combined, and restoration of signals that have been distorted in some way. In this present work, FIR filter is designed using Genetic Algorithm (GM) and its comparison is done with Kaiser window function parameters. Out of the two techniques, GA offers a quick, simple and automatic method of designing low pass FIR filters that are very close to optimum in terms of magnitude response, frequency response and in terms of phase variation. Wi...

ICTACT Journal on Soft Computing, 2010

In this paper, a new technique is presented for the design and optimization of digital FIR filters with coefficients that are presented in canonic signed-digit (CSD) format. Since such implementation requires no multipliers, it reduces the hardware cost and lowers the power consumption. The proposed technique considers three goals, the optimum number of coefficients, the optimum wordlength, and the optimum set of coefficients which satisfies the desirable frequency response and ensures the minimum hardware cost by minimizing the number of nonzero digits in CSD representation of the coefficients using Genetic Algorithms (GA). Comparing with equiripple method, the proposed technique results in about 30-40 percent reduction in hardware cost.

Diyala Journal of Engineering Sciences, 2013

Genetic Algorithms (GAs) are used to solve many optimization problems in science and engineering such as pattern recognition, robotics, biology, medicine, and many other applications. The aim of this paper is to describe a method of designing Finite Impulse Response (FIR) filter using Genetic Algorithm (GA). In this paper, the Genetic Algorithm not only used for searching the optimal coefficients, but also it is used to find the minimum number of Taps, and hence minimize the number of multipliers and adders that can be used in the design of the FIR filter. The Evolutionary Programming is the best search procedure and most powerful than Linear Programming in providing the optimal solution that is desired to minimize the ripple content in both passband and stopband. The algorithm generates a population of genomes that represents the filter coefficient and the number of taps, where new genomes are generated by crossover and mutation operations methods. Our proposed genetic technique ha...

Digital Signal Processing, 2010

In this paper, the genetic algorithm (GA) based on Canonic Signed Digit (CSD) code was used to find the optimum design of a finite impulse response digital filter (FIR). By using the characteristics of the CSD structure, the circuit was able to be simplified and also the calculation speed was raised to increase the hardware's efficiency. However, CSD structure cannot be guaranteed by a general GA after the evolution of chromosomes. Thus in this research an algorithm was proposed which the CSD structure can be maintained. A CSD coded GA was used to the evolution of chromosome to reduce the time wasted by trials and errors during the evolution and then to accelerate the training speed. In this paper, a new hybrid code for the filter coefficients was proposed to improve the precision of the coefficient of FIR. An example is shown in this paper to verify the efficiency of the proposed algorithm.

Journal of Ambient Intelligence and Humanized Computing, 2019

Nowadays, optimal and intelligent design approaches are vital in almost all areas of engineering. Scientists and engineers are attempting to make frameworks and models more proficient and intelligent. This paper deals with a detailed investigation on design of various digital filters using optimization algorithms. Generally digital filters are classified into two types which are FIR and IIR filters and are again classified into one dimensional, two dimensional and three dimensional filters for signal, image and video respectively. The design of a digital filter that satisfies all the required conditions perfectly is a challenging factor. So, apart from the conventional mathematical methods, optimization algorithms can be used to design optimal digital filters. IIR Filters are infinite impulse response filter; they have impulse response of infinite duration. FIR Filters are finite impulse response filters; they have impulse response of finite duration. In this paper we have discussed the design of various optimal digital filters based on various optimization algorithms, for processing of signal, image and video. The design of digital filters based on Evolutionary algorithms and swarm intelligence algorithms like Genetic Algorithm,

The research on optimal design of Finite impulse response (FIR) filter based on various optimization techniques, including evolutionary algorithm (EAs) have gained much attention in recent years. Genetic algorithm is a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. The New Differential evolution algorithm based on reserve genes (Eclectic Differential Evolution) have been used here for the design of finite impulse response filters. This algorithm is applied in order to obtain the actual filter response as close as possible to the ideal response. In this method, the new vectors can be produced by the combination of genes of selected chromosomes. These new vectors as the individuals are evolved with other individuals in the population and also increase the diversity of population.

1999

A genetic algorithm (GA) methodology is developed to design linear phase finite impulse response (FIR) filters, incrementally. Traditional GAS initialize the population with random generated individuals and evolve that population. When this method is applied to design medium to high complexity FIR filters, GA can not find the global minimum, easily. In order to overcome this problem, this paper proposes an incremental evolution strategy. The method starts by evolving filter with a small number of taps. After convergence, the number of taps is increased and population is initialized with the result of previous evolution. This process is continued until a predefined error level is reached. The method out performs the well known FIR filter design methods: window based design, Parks and McClellan equiripple algorithm, and least squares design.

This project presents optimal design of digital FIR and IIR filters using evolutionary optimization methods. Some evolutionary optimization methods named as Normal Particle Swarm Optimization (PSO) , PSO with Constriction Factor and Inertia Weight Approach (PSOCFIWA), PSOCFIWA with Wavelet Mutation (PSOCFIWA-WM), Harmonic Search (HS), and Harmonic Search with Wavelet Mutation (HS-WM) are discussed in this project work and have been used for the optimal design of digital low pass, high pass, band pass and band stop filters. In order to show the comparative effectiveness of the discussed algorithms, the simulation results have been compared with the already existing well established results. Further to demonstrate the efficacy of the proposed methods, these have been implemented via simulink models in MATLAB.

2008

In recent years, genetic algorithms (GAs) began to be used in many disciplines such as pattern recognition, robotics, biology, and medicine to name just a few. GAs are based on Darwin's principle of natural selection which happens to be a slow process and, as a result, these algorithms tend to require a large amount of computation. However, they offer certain advantages as well over classical gradientbased optimization algorithms such as steepest-descent and Newton-type algorithms. For example, having located local suboptimal solutions they can discard them in favor of more promising local solutions and, therefore, they are more likely to obtain better solutions in multimodal problems. By contrast, classical optimization algorithms though very efficient, they are not equipped to discard inferior local solutions in favour of more optimal ones.