Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques: Studies in Computational Intelligence, cartea 294
Autor Manuel Barros, Jorge Guilherme, Nuno Hortaen Limba Engleză Paperback – 28 mai 2012
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Paperback (1) | 638.43 lei 6-8 săpt. | |
Springer Berlin, Heidelberg – 28 mai 2012 | 638.43 lei 6-8 săpt. | |
Hardback (1) | 644.63 lei 6-8 săpt. | |
Springer Berlin, Heidelberg – 22 apr 2010 | 644.63 lei 6-8 săpt. |
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Specificații
ISBN-13: 9783642263231
ISBN-10: 3642263232
Pagini: 256
Ilustrații: 240 p. 141 illus.
Dimensiuni: 155 x 235 x 13 mm
Greutate: 0.36 kg
Ediția:2010
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3642263232
Pagini: 256
Ilustrații: 240 p. 141 illus.
Dimensiuni: 155 x 235 x 13 mm
Greutate: 0.36 kg
Ediția:2010
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
State-of-the-Art on Analog Design Automation.- Evolutionary Analog IC Design Optimization.- Enhanced Techniques for Analog Circuits Design Using SVM Models.- Analog IC Design Environment Architecture.- Optimization of Analog Circuits and Systems - Applications.- Conclusions.
Textul de pe ultima copertă
The microelectronics market trends present an ever-increasing level of complexity with special emphasis on the production of complex mixed-signal systems-on-chip. Strict economic and design pressures have driven the development of new methods to automate the analog design process. However, and despite some significant research efforts, the essential act of design at the transistor level is still performed by the trial and error interaction between the designer and the simulator.
This book presents a new design automation methodology based on a modified genetic algorithm kernel, in order to improve efficiency on the analog IC design cycle. The proposed approach combines a robust optimization with corner analysis, machine learning techniques and distributed processing capability able to deal with multi-objective and constrained optimization problems. The resulting optimization tool and the improvement in design productivity is demonstrated for the design of CMOS operational amplifiers.
This book presents a new design automation methodology based on a modified genetic algorithm kernel, in order to improve efficiency on the analog IC design cycle. The proposed approach combines a robust optimization with corner analysis, machine learning techniques and distributed processing capability able to deal with multi-objective and constrained optimization problems. The resulting optimization tool and the improvement in design productivity is demonstrated for the design of CMOS operational amplifiers.
Caracteristici
Innovative approach to a analog IC design optimization Efficient methodology to solve multi-objective multi-constraint problem Competitive results to the state-of-the-art in Analog IC Design Automation