Search

The component value selection for active analogue filter design is an important issue to improve the performances and to make compatible with existing parameters value. The Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) are efficient intelligent evolutionary algorithms for solving optimization problems. Both techniques are used to minimize the total design error of a 4th order Butterworth low pass active filter. This would be realized with component values which are compatible with E12 series. In addition, stability of filter is guaranteed by minimization of gain sensitivity product. By considering the minimization of Gain Sensitivity Product (GSP), the sixteen variables of objective function are reduced to eight variables which speed up the iteration process. The simulation results prove the efficiency of algorithms for the design of analogue active filter by optimizing the component values based on E12 compatible with minimization of GSP by minimising the design error. [ABSTRACT FROM AUTHOR]

In conventional design method, the parameters of an active filter cannot be selected based on manufactured parameters such as E series. However, this can be feasibly obtained using optimization technique through an exhaustive search on all possible combination of preferred values of E series by providing the coding system. The exhaustive search for low pass second order filter is carried out through Meta-heuristic optimization Algorithms. The cost function for error minimization including compatible to E series and gain adaptive are used here as objective function for finding out minima through well known algorithms. The inclusion of gain constrained concept eliminates the extra op-amp cascading to state variable filter circuit which reduces the noise and dimension of cost function; in addition, improves the stability of the system. The performances of Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA) and Sine-Cosine Algorithm (SCA), nature and physics-inspired artificial intelligence techniques are evaluated for state variable filter design considering gain constrained. Each of above algorithms is applied to filter structure and performances of them are also evaluated when filter design is realized with components selected from different manufactured E series. [ABSTRACT FROM AUTHOR]