%0 Journal Article %1 rajibkardurbadalmandal2011filter %A Rajib Kar, Durbadal Mandal, Dibbendu Roy %A Ghoshal, Sakti Prasad %D 2011 %E Das, Dr Vinu V %J International Journal on Electrical and Power Engineering %K Convergence FIR_Filter GA Optimization PSO %N 2 %P 5 %T FIR Filter Design using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach %U http://doi.searchdl.org/01.IJEPE.2.2.162 %V 2 %X This paper presents an alternative approach for the design of linear phase digital low pass FIR filter using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSO-CFIWA). FIR filter design is a multimodal optimization problem. The conventional gradient based optimization techniques are not efficient for digital filter design. Given the filter specification to be realized, PSO algorithm generates a set of filter coefficients and tries to meet the ideal frequency characteristic. In this paper, for the given problem, the realization of the FIR filters of different order has been performed. The simulation results have been compared with the well accepted evolutionary algorithm such as genetic algorithm (GA). The results justify that the proposed filter design approach using PSO-CFIWA outperforms to that of GA, not only in the accuracy of the designed filter but also in the convergence speed and solution quality

@article{rajibkardurbadalmandal2011filter, abstract = {This paper presents an alternative approach for the design of linear phase digital low pass FIR filter using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSO-CFIWA). FIR filter design is a multimodal optimization problem. The conventional gradient based optimization techniques are not efficient for digital filter design. Given the filter specification to be realized, PSO algorithm generates a set of filter coefficients and tries to meet the ideal frequency characteristic. In this paper, for the given problem, the realization of the FIR filters of different order has been performed. The simulation results have been compared with the well accepted evolutionary algorithm such as genetic algorithm (GA). The results justify that the proposed filter design approach using PSO-CFIWA outperforms to that of GA, not only in the accuracy of the designed filter but also in the convergence speed and solution quality}, added-at = {2012-09-22T08:09:21.000+0200}, author = {{Rajib Kar, Durbadal Mandal}, Dibbendu Roy and Ghoshal, Sakti Prasad}, biburl = {https://www.bibsonomy.org/bibtex/228a6be8ef1df45edf6077f2932a7ca05/ideseditor}, editor = {Das, Dr Vinu V}, interhash = {2330512712049753cdcdf3d08e769f69}, intrahash = {28a6be8ef1df45edf6077f2932a7ca05}, journal = {International Journal on Electrical and Power Engineering}, keywords = {Convergence FIR_Filter GA Optimization PSO}, month = {August}, number = 2, pages = 5, timestamp = {2012-09-22T08:09:21.000+0200}, title = {FIR Filter Design using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach}, url = {http://doi.searchdl.org/01.IJEPE.2.2.162}, volume = 2, year = 2011 }