Abstract
Simulation programs are widely used in the design of analog electronic circuits to analyze their behavior and to predict the response of a circuit to variations in the circuit components. A fuzzy inference system (FIS) in combination with these simulation tools can be applied to identify both the main and interaction effects of circuit parameters on the response variables, which can help to optimize them. This paper describes an application of fuzzy inference systems to modeling the behavior of analog electronic circuits for further optimization. First, a Monte Carlo analysis, generated from the tolerances of the circuit components, is performed. Once the Monte Carlo results are obtained for each of the response variables, the fuzzy inference systems are generated and then optimized using a particle swarm optimization (PSO) algorithm. These fuzzy inference systems are used to determine the influence of the circuit components on the response variables and to select them to optimize the amplifier design. The methodology proposed in this study can be used as the basis for optimizing the design of similar analog electronic circuits.
Highlights
Fuzzy inference systems (FISs) are powerful tools for analyzing the behavior of electronic circuits to optimize circuit design
The results obtained for the voltage gain (Av) are provided, those for the total harmonic distortion (THD)
As in the previous case, this FIS was capable of accurately modeling the THD and, its behavior
Summary
Fuzzy inference systems (FISs) are powerful tools for analyzing the behavior of electronic circuits to optimize circuit design. They can be used for modeling the response of electronic circuit variables and to simultaneously identify the influence of circuit parameters on an output response. Circuit optimization presents some drawbacks due to the non-linearities in the components that affect the response. The use of optimization techniques without feedback from the design process can lead to impractical solutions because the optimized values may not be feasible due to the tolerances of some components and the instabilities that may be generated within the circuit. The solution of the optimization process should be verified so that the circuit will remain stable despite any variations in the tolerances of the components
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