Skip to main content
SpringerLink
Log in

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

  • ISNN 2008
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. 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

    Article  Google Scholar 

  2. 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

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  4. 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)

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

  6. 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

    Article  Google Scholar 

  7. 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 11:4–21

    Article  Google Scholar 

  8. 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

  9. 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

    Google Scholar 

  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

    Article  Google Scholar 

  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., Singapore

    MATH  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  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 51:1339–1350

    Article  Google Scholar 

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

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank Jeroen Croon from the NXP-TSMC Research Center, Device Modeling Department, Eindhoven, The Netherlands for the LNA simulation code and Joost Rommes from Corporate I&T/Design Technology & Flows, NXP Semiconductors, Eindhoven for the many fruitful discussions. This work was supported by FWO Flanders, the Science and Innovation Administration Flanders, and the O-MOORE-NICE! project as supported by the European Commission through the Marie Curie program under contract number MTKI-CT-2006-042477.

Author information

Authors and Affiliations

  1. Department of Information Technology (INTEC), Ghent University, IBBT, Gaston Crommenlaan 8, 9050, Gent, Belgium

    Dirk Gorissen & Tom Dhaene

  2. University of Antwerp and NXP Semiconductors, Corporate I&T/TF/DM, Physical Design Methods High Tech Campus 37, PostBox WY4-01, 5656 AE, Eindhoven, The Netherlands

    Luciano De Tommasi

  3. Department of Maths and Computer Science, Antwerp University, Middelheimlaan 1, 2020, Antwerp, Belgium

    Karel Crombecq

Authors
  1. Dirk Gorissen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luciano De Tommasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karel Crombecq
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tom Dhaene
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dirk Gorissen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gorissen, D., De Tommasi, L., Crombecq, K. et al. Sequential modeling of a low noise amplifier with neural networks and active learning. Neural Comput & Applic 18, 485–494 (2009). https://doi.org/10.1007/s00521-008-0223-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-008-0223-1

Keywords

Search

Navigation