Sequential modeling of a low noise amplifier with neural networks and active learning
- Author
- Dirk Gorissen (UGent) , Luciano De Tommasi, Karel Crombecq and Tom Dhaene (UGent)
- Organization
- 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.
- Keywords
- Global surrogate modeling, Active learning, Amplifier, DESIGN
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-792506
- MLA
- Gorissen, Dirk, et al. “Sequential Modeling of a Low Noise Amplifier with Neural Networks and Active Learning.” NEURAL COMPUTING & APPLICATIONS, vol. 18, no. 5, 2009, pp. 485–94, doi:10.1007/s00521-008-0223-1.
- APA
- Gorissen, D., De Tommasi, L., Crombecq, K., & Dhaene, T. (2009). Sequential modeling of a low noise amplifier with neural networks and active learning. NEURAL COMPUTING & APPLICATIONS, 18(5), 485–494. https://doi.org/10.1007/s00521-008-0223-1
- Chicago author-date
- Gorissen, Dirk, Luciano De Tommasi, Karel Crombecq, and Tom Dhaene. 2009. “Sequential Modeling of a Low Noise Amplifier with Neural Networks and Active Learning.” NEURAL COMPUTING & APPLICATIONS 18 (5): 485–94. https://doi.org/10.1007/s00521-008-0223-1.
- Chicago author-date (all authors)
- Gorissen, Dirk, Luciano De Tommasi, Karel Crombecq, and Tom Dhaene. 2009. “Sequential Modeling of a Low Noise Amplifier with Neural Networks and Active Learning.” NEURAL COMPUTING & APPLICATIONS 18 (5): 485–494. doi:10.1007/s00521-008-0223-1.
- Vancouver
- 1.Gorissen D, De Tommasi L, Crombecq K, Dhaene T. Sequential modeling of a low noise amplifier with neural networks and active learning. NEURAL COMPUTING & APPLICATIONS. 2009;18(5):485–94.
- IEEE
- [1]D. Gorissen, L. De Tommasi, K. Crombecq, and T. Dhaene, “Sequential modeling of a low noise amplifier with neural networks and active learning,” NEURAL COMPUTING & APPLICATIONS, vol. 18, no. 5, pp. 485–494, 2009.
@article{792506,
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.}},
author = {{Gorissen, Dirk and De Tommasi, Luciano and Crombecq, Karel and Dhaene, Tom}},
issn = {{0941-0643}},
journal = {{NEURAL COMPUTING & APPLICATIONS}},
keywords = {{Global surrogate modeling,Active learning,Amplifier,DESIGN}},
language = {{eng}},
location = {{Beijing, PR China}},
number = {{5}},
pages = {{485--494}},
title = {{Sequential modeling of a low noise amplifier with neural networks and active learning}},
url = {{http://doi.org/10.1007/s00521-008-0223-1}},
volume = {{18}},
year = {{2009}},
}
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