nach oben

Neural Computing and Applications

Erschienen in:

01.06.2009 | ISNN 2008

Sequential modeling of a low noise amplifier with neural networks and active learning

verfasst von: Dirk Gorissen, Luciano De Tommasi, Karel Crombecq, Tom Dhaene

Erschienen in: Neural Computing and Applications | Ausgabe 5/2009

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The use of global surrogate models has become commonplace as a cost effective alternative for performing complex high fidelity computer simulations. Due to their compact formulation and negligible evaluation time, global surrogate models are very useful tools for exploring the design space, what-if analysis, optimization, prototyping, visualization, and sensitivity analysis. Neural networks have been proven particularly useful in this respect due to their ability to model high dimensional, non-linear responses accurately. In this article, we present the results of an extensive study on the performance of neural networks as compared to other modeling techniques in the context of active learning. We investigate the scalability and accuracy in function of the number design variables and number of datapoints. The case study under consideration is a high dimensional, parametrized low noise amplifier RF circuit block.

Vorheriger Artikel A gradient-based sequential radial basis function neural network modeling method

Nächster Artikel Self-localization and obstacle avoidance for a mobile robot

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Anastasiadis D, Magoulas D (2006) Analysing the localisation sites of proteins through neural networks ensembles. Neural Comput Appl 15(3):277–288. doi:10.1007/s00521-006-0029-y CrossRef

Busby D, Farmer CL, Iske A (2007) Hierarchical nonlinear approximation for experimental design and statistical data fitting. SIAM J Sci Comput 29(1):49–69. doi:10.1137/050639983 MATHCrossRefMathSciNet

Chen J, Adams BJ (2006) Integration of artificial neural networks with conceptual models in rainfall-runoff modeling. J Hydrol 318:232–249CrossRef

Clarke SM, Griebsch JH, Simpson TW (2003) Analysis of support vector regression for approximation of complex engineering analyses. In: Proceedings of the 29th design automation conference (ASME Design Engineering Technical Conferences) (DAC/DETC’03)

Crombecq K (2007) A gradient based approach to adaptive metamodeling. Tech. rep., University of Antwerp

Devabhaktuni V, Chattaraj B, Yagoub M, Zhang QJ (2003) Advanced microwave modeling framework exploiting automatic model generation, knowledge neural networks, and space mapping. IEEE Trans Microw Theory Tech 51(7):1822–1833. doi:10.1109/TMTT.2003.814318 CrossRef

Devabhaktuni V, Yagoub M, Fang Y, Xu J, Zhang Q (2001) Neural networks for microwave modeling: model development issues and nonlinear modeling techniques. Int J RF Microw CAE 11:4–21CrossRef

Foresee F, Hagan M (1997) Gauss-newton approximation to bayesian regularization. In: Proceedings of the 1997 international joint conference on neural networks, pp 1930–1935

Ganser M, Grossenbacher K, Schutz M, Willmes L, Back T (2007) Simulation meta-models in the early phases of the product development process. In: Proceedings of efficient methods for robust design and optimization (EUROMECH 07)

10.

Gorissen D (2007) Heterogeneous evolution of surrogate models. Master’s thesis, Master of AI, Katholieke Universiteit Leuven (KUL)

11.

Gorissen D, Hendrickx W, Crombecq K, Dhaene T (2007) Adaptive distributed metamodeling. In: Dayde M et al (eds) Proceedings of 7th international meeting on high performance computing for computational science (VECPAR 2006). Lecture notes in computer science, vol 4395. Springer, Hiedelberg, pp 579–588

12.

Gorissen D, De Tommasi L, Croon J, Dhaene T (2008) Automatic model type selection with heterogeneous evolution: an application to rf circuit block modeling. In: Proceedings of the IEEE congress on evolutionary computation, WCCI 2008, Hong Kong

13.

Gorissen D, De Tommasi L, Hendrickx W, Croon J, Dhaene T (2008) Rf circuit block modeling via kriging surrogates. In: Proceedings of the 17th international conference on microwaves, radar and wireless communications (MIKON 2008)

14.

Hendrickx W, Gorissen D, Dhaene T (2006) Grid enabled sequential design and adaptive metamodeling. In: WSC ’06: Proceedings of the 37th conference on winter simulation. Winter Simulation Conference, pp 872–881

15.

Lophaven SN, Nielsen HB, Søndergaard J (2002) Aspects of the matlab toolbox DACE. Tech. rep., Informatics and Mathematical Modelling, Technical University of Denmark, DTU, Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby

16.

MacKay DJC (1992) Bayesian model comparison and backprop nets. In: Moody JE, Hanson SJ, Lippmann RP (eds) Advances in neural information processing systems 4. Morgan Kaufmann, San Mateo, pp 839–846

17.

Pao HT, Chih YY (2006) Comparison of tscs regression and neural network models for panel data forecasting: debt policy. Neural Comput Appl 15(2):117–123. doi:10.1007/s00521-005-0014-x CrossRef

18.

Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, Cambridge

19.

Suykens J, Gestel TV, Brabanter JD, Moor BD, Vandewalle J (2002) Least squares support vector machines. World Scientific Publishing Co., Pte, Ltd., SingaporeMATH

20.

Ye K, Li W, Sudjianto A (2000) Algorithmic construction of optimal symmetric latin hypercube designs. J Stat Plan Inference 90:145–159MATHCrossRefMathSciNet

21.

Zhang Q, Gupta K, Devabhaktuni V (2003) Artificial neural networks for RF and microwave design: from theory to practice. IEEE Trans Microw Theory Tech 51:1339–1350CrossRef

22.

Zhang QJ, Gupta KC (2000) Neural networks for RF and microwave design (Book + Neuromodeler Disk). Artech House, Inc., Norwood

Titel: Sequential modeling of a low noise amplifier with neural networks and active learning
verfasst von: Dirk Gorissen
Luciano De Tommasi
Karel Crombecq
Tom Dhaene
Publikationsdatum: 01.06.2009
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 5/2009
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-008-0223-1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 5/2009

Intelligent methods for solving inverse problems of backscattering spectra with noise: a comparison between neural networks and simulated annealing

AI-SIMCOG: a simulator for spiking neurons and multiple animats’ behaviours

Editorial to special issue: computational intelligence for optimization, modeling and control

A motion location based video watermarking scheme using ICA to extract dynamic frames

A gradient-based sequential radial basis function neural network modeling method

Traffic congestion identification by combining PCA with higher-order Boltzmann machine

Premium Partner