Novel Application Of Some Modern Computational Heuristic Paradigms To Active Noise Control Systems

In this dissertation,a novel applications of modern computational heuristic paradigms(CHP)are presented to parameter identification problem of nonlinear active noise control(ANC)arising in the fields of signal processing systems.The evolutionary as well as swarming inspired CHPs are exploited for the design of ANC based controllers represented with the competency of nonlinear Volterra filtering technique optimized through global search strength of Backtracking Search algorithm(BSA),firework algorithm(FWA)and grasshopper optimization algorithm(GOP).The optimization mechanism CHPs is applied to minimize the cost function of ANC system based on linear/nonlinear and primary/secondary paths with sinusoidal,random,and complex random interfering signals.The comparative study through results of statistical observations in terms of accuracy,convergence and complexity measures reveals that the proposed optimization solvers based ANC controllers are effective,reliable,robust and stable.(1)Integrated strength of backtracking search algorithm(BSA)and sequential quadratic programming(SQP)is exploited for nonlinear active noise control(ANC)systems based on finite impulse response(FIR)and Volterra filtering procedures.Global search efficacy of BSA aided with rapid local refinements with SQP is practiced for effective optimization of fitness function for ANC systems having sinusoidal,random and complex random signals under several variants based on linear/nonlinear and primary/secondary paths.Statistical observations demonstrated the worth of stochastic solvers BSA and BSA-SQP by means of accuracy,convergence and complexity indices.(2)The strength of recently introduced firework algorithms(FWA)and its variants are exploited in the field of system identification based on active noise control(ANC)systems.Residual error based fitness function is constructed for(ANC)system and optimization of parameter is carried out with the help of firework,enhance firework,adaptive firework and basic firework algorithms.(3)Novel application of integrated swarming intelligence computing paradigm is exploited for reliable treatment of nonlinear active noise control(ANC)systems using global search capacity of grasshopper optimization algorithm(GOA)combined with local search efficacy of sequential quadratic programming(SQP),i.e.,GOA-SQP.The comparison of the results through statistical observation in terms of accuracy,convergence and complexity indices reveals that the GOA-SQP based ANC controllers are operative,resilient and viable.