Digital filter design
Design of arbitrary FIR log filters by genetic algorithm approach
Signal Processing
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Linear phase FIR filter design using particle swarm optimization and genetic algorithms
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A canonic-signed-digit coded genetic algorithm for designing finite impulse response digital filter
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Particle swarm optimization with quantum infusion for system identification
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DEPSO and PSO-QI in digital filter design
Expert Systems with Applications: An International Journal
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ICICTA '11 Proceedings of the 2011 Fourth International Conference on Intelligent Computation Technology and Automation - Volume 02
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ISED '11 Proceedings of the 2011 International Symposium on Electronic System Design
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IEEE Transactions on Signal Processing
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IEEE Transactions on Signal Processing
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This paper presents an efficient way of designing linear phase finite impulse response (FIR) low pass and high pass filters using a novel algorithm ADEPSO. ADEPSO is hybrid of fitness based adaptive differential evolution (ADE) and particle swarm optimization (PSO). DE is a simple and robust evolutionary algorithm but sometimes causes instability problem; PSO is also a simple, population based robust evolutionary algorithm but has the problem of sub-optimality. ADEPSO has overcome the above individual disadvantages faced by both the algorithms and is used for the design of linear phase low pass and high pass FIR filters. The simulation results show that the ADEPSO outperforms PSO, ADE, and DE in combination with PSO not only in magnitude response but also in the convergence speed and thus proves itself to be a promising candidate for designing the FIR filters.