Abstract
This paper presents the design of Optimal Infinite-Impulse Response (IIR) digital filters using Genetic Algorithm (GA). IIR filter is essentially a digital filter with Recursive responses. Since the error surface of digital IIR filters is generally nonlinear and multimodal, global optimization techniques are required in order to avoid local minima. This paper presents heuristic way for the designing IIR filters. GA is a powerful global optimization algorithm introduced in combinatorial optimization problems. The paper finds the optimum Coefficients of IIR digital filter through GA. Design of Lowpass and High pass IIR digital filter is proposed to provide estimate of transition band. It is found that the calculated values are more optimal than fda tool available for the design of filter in MATLAB. The simulation result of the employed examples shows an improvement on transition band and mean-square-error (MSE). The position of pole-zero is also presented to describe stability and results are compared with Simulated Annealing (SA) method.
Highlights
Over the last few decades the field of Digital Signal Processing (DSP) has grown to important both theoreticcally and technologically
This paper presents the design of Optimal Infinite-Impulse Response (IIR) digital filters using Genetic Algorithm (GA)
Because the error surface of IIR filters is usually nonlinear and multimodal, conventional gradient-based design methods may get stuck in the local minima of error surface [4,5].some researchers have attempted to develop design methods based on modern heuristic optimization algorithms such as genetic algorithm (GA) [6,7,8,9], simulated annealing (SA), tabu search (TS) [10] etc
Summary
Over the last few decades the field of Digital Signal Processing (DSP) has grown to important both theoreticcally and technologically. In DSP, there are two important types of Systems. The first type of systems performs signal filtering in time domain and it is known as Digital filters. The second type of systems provide signal representation frequency domain and are known as Spectrum Analyzer. Digital filters are classified either as Finite duration impulse response (FIR) filters or Infinite duration impulse response (IIR) filters, depending on the form of impulse response of the system. Digital infinite-impulse-response (IIR) filters can often provide a much better performance and less computational cost than their equivalent finite-impulse-response (FIR) filters and have become the target of growing interest [1,2,3,4]. Because the error surface of IIR filters is usually nonlinear and multimodal, conventional gradient-based design methods may get stuck in the local minima of error surface [4,5].some researchers have attempted to develop design methods based on modern heuristic optimization algorithms such as genetic algorithm (GA) [6,7,8,9], simulated annealing (SA), tabu search (TS) [10] etc
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