A fuzzy optimization method for CMOS operational amplifier design

Oltean, Gabriel; Miron, C.; Zahan, S.; Gordan, Mihaela

doi:10.1109/neurel.2000.902404

Cited by 6 publications

(3 citation statements)

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“…Several studies can be found in the literature dealing with the application of fuzzy systems [3,4]. Likewise, Oltean et al [5] studied the application of various types of FISs both for modeling and designing electronic circuits; they proposed the application of a fuzzy optimization method to a CMOS operational amplifier. They employed an adaptive neuro-fuzzy inference system (ANFIS) to tune the initial zero-order Sugeno FIS.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of a Fuzzy Inference System for Optimization of an Amplifier Design

Dieste-Velasco

2021

Mathematics

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Application of a Fuzzy Inference System for Optimization of an Amplifier Design

Dieste-Velasco

2021

Mathematics

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“…The information can be used to estimate the optimal values of these parameters by means of an expert system. A fuzzy inference system embedded in an optimization loop is used for this purpose [10]. However, the influence of a number of parameters on the simulation results cannot be predicted precisely.…”

Section: Parameter Identificationmentioning

confidence: 99%

“…It adjusts the parameter values for a given set of GA optimization parameters to give the best possible model response [10]. The block diagram of this inner optimization loop is shown in figure 10.…”

Section: Fuzzy Expert Systemmentioning

confidence: 99%

Parameter identification for a complex lead-acid battery model by combining fuzzy control and stochastic optimization

Steiner

Schweighofer

2006

Inverse Problems in Science and Engineering

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The accurate simulation of lead-acid batteries requires the use of sophisticated models based on first principles containing many parameters. Existing methods for parameter identification often fail due to many local minima of the error function or the high computational needs to cover the parameter space. Therefore, a novel approach for parameter identification with complex physical models containing many unknown parameters is presented. It is based on the utilization of available expert knowledge regarding specific model parameters. The expert knowledge is integrated through fuzzy control and combined with stochastic optimization algorithms for solving the battery identification problem.

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