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- W2227244036 abstract "The thesis focuses on the application of computational intelligence (CI) techniques for two problemssystem identification and digital filter design. In system identification, different case studies have been carried out with equal or reduced number of orders as the original system and also in identifying a blackbox model. Lowpass, Highpass, Bandpass and Bandstop FIR and Lowpass IIR filters have been designed using three algorithms using two different fitness functions. Particle Swarm Optimization (PSO), Differential Evolution based PSO (DEPSO) and PSO with Quantum Infusion (PSO-QI) algorithms have been applied in this work. PSO-QI is a new hybrid algorithm where global best particle (gbest) obtained from PSO goes into a tournament with an offspring produced by mutating the gbest of PSO using the quantum principle in Quantum behaved PSO (QPSO) and the winner is selected as the new gbest of the swarm. In QPSO, unlike traditional PSO, exact values of particle’s position and velocity cannot be determined. However, its position in the solution space is determined by mapping the probability of its appearance in the quantized search space. The results obtained from PSO-QI have been compared with the DEPSO hybrid algorithm and the classical PSO. In all of the cases, PSO-QI has outperformed the other two algorithms in its ability to converge to the lowest error value and its consistency in finding the solution every time and thus proven to be the best. However, the computational complexity of PSO-QI is higher than that of the other two algorithms." @default.
- W2227244036 created "2016-06-24" @default.
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- W2227244036 date "2009-01-01" @default.
- W2227244036 modified "2023-09-27" @default.
- W2227244036 title "Applications of swarm, evolutionary and quantum algorithms in system identification and digital filter design" @default.
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