Automatic circuit simplification for meaningful symbolic analysis using the genetic algorithm

Pookaiyaudom, S.; Jantarang, S.

doi:10.1109/iscas.1996.539820

Cited by 2 publications

(3 citation statements)

References 3 publications

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“…Hence to overcome this disadvantage of simplification, a new multi-objective SAG methodology is proposed. 12,13 Efficient design methods are needed since analog signal processing constantly demands higher bandwidth and better noise performances. 14,15 The study of noises in analog circuits is important because these limit the amplitude of the electrical signal that can be processed by the analog circuits.…”

Section: Introductionmentioning

confidence: 99%

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An efficient method to compute simplified noise parameters of analog amplifiers using symbolic and evolutionary approach

Panda

Patnaik

Mal

2020

Int J Numerical Modelling

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Symbolic circuit analysis suffers as the symbolic terms increases by many folds with the increase in the size of the circuit. Hence simplification techniques are used for the analysis of the circuits which are larger in size. In this work, multi-objective evolutionary optimisation algorithms for obtaining simplified symbolic expressions of analog amplifier circuits are presented which are not only very efficient in terms of speed and the amount of circuit reduction but also solve the accuracy problems. Initially, all MOSFETs are replaced with their nullor equivalent model. Then, the formulation of accurate fullysymbolic noise expressions is carried out by the application of symbolic nodal analysis. The derived exact symbolic expressions are then simplified using an evolutionary approach. This method has been successfully coded in MATLAB. The numerical results obtained before and after simplification are compared with SPICE simulation to demonstrate the efficiency of the proposed methodology.

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Section: Introductionmentioning

confidence: 99%

“…Approximation after generation is reliable but initially, we have to expand the product terms. Hence to overcome this disadvantage of simplification, a new multi‐objective SAG methodology is proposed 12,13 …”

Section: Introductionmentioning

confidence: 99%

An efficient method to compute simplified noise parameters of analog amplifiers using symbolic and evolutionary approach

Panda

Patnaik

Mal

2020

Int J Numerical Modelling

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“…One result is very large expressions for performance measurements. These are computationally expensive to solve (in [13], it is shown that the gain expression of a 3 transistor circuit consist of 63 distinct terms in numerator and 908 distinct terms in denominator). Another factor in increased complexity is that many performance measurements do not have closed form expressions.…”

Section: Circuit Sizing: Complex Behavior Models and Interconnection mentioning

confidence: 99%

Design of Posynomial Models for Mosfets: Symbolic Regression Using Genetic Algorithms

Aggarwal

O’Reilly

Genetic Programming Theory and Practice IV

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Starting from a broad description of analog circuit design in terms of topology design and sizing, we discuss the difficulties of sizing and describe approaches that are manual or automatic. These approaches make use of blackbox optimization techniques such as evolutionary algorithms or convex optimization techniques such as geometric programming. Geometric programming requires posynomial expressions for a circuit's performance measurements. We show how a genetic algorithm can be exploited to evolve a posynomial expression (i.e. model) of transistor (i.e. mosfet) behavior more accurately than statistical techniques in the literature.

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Automatic circuit simplification for meaningful symbolic analysis using the genetic algorithm

Cited by 2 publications

References 3 publications

An efficient method to compute simplified noise parameters of analog amplifiers using symbolic and evolutionary approach

An efficient method to compute simplified noise parameters of analog amplifiers using symbolic and evolutionary approach

Design of Posynomial Models for Mosfets: Symbolic Regression Using Genetic Algorithms

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