Analog Active Filter Design Research Articles

Optimal components selection for active filter design with average differential evolution algorithm

Component selection in electronic circuit design is an important issue to achieve a targeted performance and quality level. Particularly in filter circuits, changes in gain and phase response of the circuit are directly dependent on the component values of the filter. Therefore, the selection of component values must be done carefully. In this article, a differential evolution (DE) algorithm with a new mutation strategy, named as average DE (ADE), is introduced for analog active filter design. The ADE algorithm minimizes the total design error of the state variable filter (SVF) for the optimal selection of component values. Simulation results indicate that the proposed algorithm reduces the design error when compared to other methods.

Analog active filter design using a multi objective genetic algorithm

The selection of the correct values for passive elements, resistors, and capacitors, is an important task in analog active filter design. The classic method of choosing passive elements is a difficult task and can lead to errors. To reduce the incidence of error and human effort evolutionary optimization techniques are used to select the values of capacitors and resistors. However, due to the single objective optimization technique, these are not well suited to optimize different filter parameters. For this reason, the performance of a multi-objective genetic algorithm named non-dominated sorting genetic algorithm II (NSGA-II) against the different single objective algorithms is evaluated. Two analog active filters: A fourth order Butterworth and a second order state variable filter with the operational amplifiers in their cores are used for testing purposes. In both cases two different objects are chosen along with eight components as variables to be optimized. The component values are compatible with the E12, E24 and E96 series using NSGA-II. The computation results are better in terms of design error and allow for better resistor and capacitor choice. To reach the same or better results the NSGA-II needs fewer generations compared with other genetic algorithms for this problem.

Vortex search algorithm for the analog active filter component selection problem

Optimal selection of passive components is one of the most crucial design steps in analog active filter design problems. As the number of possible combinations increases, it becomes infeasible to perform an exhaustive search for optimum component values. Thus, intelligent algorithms are required for a fast and optimum selection of the passive component values. In this study, a newly proposed metaheuristic, the vortex search (VS) algorithm, is utilized for this purpose. To evaluate the performance of the VS algorithm, two different analog active filter topologies, the fourth-order Butterworth low-pass filter and the state variable low-pass filter, are used. The component values of the active filter topologies are selected within three different series of (E12, E24 and E96) passive components for both the resistors and the capacitors. The results are compared to other metaheuristics such as the particle swarm optimization (PSO2011), artificial bee colony (ABC), differential evolution (DE) and harmony search (HS) algorithms. It is shown that, the VS algorithm finds the minimum fitness values for both of the active filter topologies which in turn provides the optimum component values for each of the used component series.

Optimal analog active filter design using craziness‐based particle swarm optimization algorithm

SummaryBecause of the manufacturing constraints, the optimal selection of passive component values for the design of analog active filter is very critical. As the search on possible combinations in preferred values for capacitors and resistors is an exhaustive process, it has to be automated with high accuracy within short computation time. Evolutionary computation may be an attractive alternative for automatic selection of optimal discrete component values such as resistors and capacitors for analog active filter design. This paper presents an efficient evolutionary optimization approach for optimal analog filter design considering different topologies and manufacturing series by selecting their component values. The evolutionary optimization technique employed is craziness‐based particle swarm optimization (CRPSO). PSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: fast convergence and only a few control parameters. However, the performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problem. To overcome these problems, the PSO algorithm has been modified to CRPSO and is used for the selection of optimal passive component values of fourth‐order Butterworth low‐pass analog active filter and second‐order state variable low‐pass filter, respectively. CRPSO performs the dual task of efficiently selecting the component values as well as minimizing the total design errors of low‐pass active filters. The component values of the filters are selected in such a way so that they become E12/E24/E96 series compatible. The simulation results prove that CRPSO efficiently minimizes the total design error with respect to previously used optimization techniques. Copyright © 2014 John Wiley & Sons, Ltd.

Optimal selection of components value for analog active filter design using simplex particle swarm optimization

The simplex particle swarm optimization (Simplex-PSO) is a swarm intelligent based evolutionary computation method. Simplex-PSO is the hybridization of Nedler–Mead simplex method and particle swarm optimization (PSO) without the velocity term. The Simplex-PSO has fast optimizing capability and high computational precision for high-dimensionality functions. In this paper, Simplex-PSO is employed for selection of optimal discrete component values such as resistors and capacitors for fourth order Butterworth low pass analog active filter and second order State Variable low pass analog active filter, respectively. Simplex-PSO performs the dual task of efficiently selecting the component values as well as minimizing the total design errors of low pass analog active filters. The component values of the filters are selected in such a way so that they become E12/E24/E96 series compatible. The simulation results prove that Simplex-PSO efficiently minimizes the total design error to a greater extent in comparison with previously reported optimization techniques.

Particle Swarm Optimization with Aging Leader and Challengers for Optimal Design of Analog Active Filters

Due to the manufacturing limitations, the task of optimal analog active filter design by hand is very difficult. Evolutionary computation may be a competent implement for automatic selection of optimal discrete component values such as resistors and capacitors for analog active filter design. This paper presents an efficient approach for optimal analog filter design considering different topologies and manufacturing series by selecting their component values. The evolutionary optimization technique used is particle swarm optimization (PSO) with Aging Leader and Challenger (ALC-PSO). ALC-PSO performs the dual-task of efficiently selecting the component values as well as minimizing the total design errors of low pass active filters. The component values of the filters are selected in such a way so that they become E12/E24/E96 series compatible. The simulation results prove that ALC-PSO efficiently minimizes the total design error with respect to previously used optimization techniques.

Analog active filter component selection with nature inspired metaheuristics

The most crucial design step for analog active filter design is the optimal selection of passive component values due to manufactured constants. Since the search on possible combinations in preferred values for capacitors and resistors is an exhaustive process, it has to be automated with high accuracy within short computation time. In this work, two nature inspired metaheuristics, differential evolution (DE) and harmony search (HS) algorithms are utilized for optimal filter design considering different topologies and manufacturing series. Simulation results show that as global optimization methods both DE and HS minimize the total design error and reduce the elapsed time with respect to previously utilized methods.

MOS compatible, all-capacitor biquadratic active filters

An analog active filter design method is proposed, using only integrating amplifiers and ratioed capacitors. The design is suitable for monolithic implementation, particularly in MOS technology. Experimental results demonstrate the circuits' performance at high frequencies.