Please use this identifier to cite or link to this item: http://drsr.daiict.ac.in//handle/123456789/273
Title: Particle swarm optimization based synthesis of analog circuits using neural network performance macromodels
Authors: Mandal, Sushanta Kumar
Saxena, Neha
Keywords: Swarm intelligence
Particles
Nuclear physics
Mathematical optimization
Neural networks
Computer science
Linear integrated circuits
Design and construction
Electronic circuit design
Analog circuit design
Issue Date: 2009
Publisher: Dhirubhai Ambani Institute of Information and Communication Technology
Citation: Saxena, Neha (2009). Particle swarm optimization based synthesis of analog circuits using neural network performance macromodels. Dhirubhai Ambani Institute of Information and Communication Technology, xi, 46 p. (Acc.No: T00236)
Abstract: This thesis presents an efficient an fast synthesis procedure for an analog circuit. The proposed synthesis procedure used artificial neural network (ANN) models in combination with particle swarm optimizer. ANN has been used to develop macro-models for SPICE simulated data of analog circuit which takes transistor sizes as input and produced circuit specification as output in negligible time. The particle swarm optimizer explore the specfied design space and generates transistor sizes as potential solutions. Several synthesis results are presented which show good accuracy with respect to SPICE simulations. Since the proposed procedure does not require an SPICE simulation in the synthesis loop, it substantially reduces the design time in circuit design optimization.
URI: http://drsr.daiict.ac.in/handle/123456789/273
Appears in Collections:M Tech Dissertations

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