Optimal Design of State Variable Filter using Partition-bound Particle Swarm Optimization Optimal Design of State Variable Filter
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Abstract
Because of manufacturing constraints, designing analog active filters is highly challenging. Evolutionary computing is an effective method for automatically selecting the component values like resistors and capacitors. This work describes the partition-bound Particle Swarm Optimization (PB-PSO) for efficiently designing second-order active low-pass state variable filter (SVF) considering different manufacturing series. PB-PSO is responsible for efficiently picking components and minimizing total design error. The filter components are chosen to be compatible with the E12/ E24/ E96 series. Compared to earlier optimization strategies, the simulation findings show that PB-PSO reduces the overall design error.
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