Skip to main content

Neural Networks for Eye Height and Eye Width Prediction with an Improved Adaptive Sampling Algorithm

  • Conference paper
  • First Online:
Modeling, Design and Simulation of Systems (AsiaSim 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 751))

Included in the following conference series:


This paper discusses the application of artificial neural networks (ANN) in terms of eye diagram modeling, where ANN models are trained to predict the eye height and eye width, which are useful information for signal integrity inspection. This paper also presents an improved version of the adaptive sampling method which is used in the data collection process. The proposed adaptive sampling manages to reduce the number of training and testing samples needed to train the neural model, thus reducing the time needed to simulate the data. In addition, the proposed adaptive sampling can generate training and testing samples more evenly across the whole design space, reducing the risk of oversampling and undersampling.

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

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. Ambasana, N., Gope, D., Mutnury, B., Anand, G.: Application of artificial neural networks for eye-height/width prediction from S-parameters. In: 2014 IEEE 23rd Conference on Electrical Performance of Electronic Packaging and Systems (2014)

    Google Scholar 

  2. Ambasana, N., Anand, G., Mutnury, B., Gope, D.: Eye height/width prediction from S-parameters using learning-based models. IEEE Trans. Compon. Packag. Manuf. Technol. 6, 873–885 (2016)

    Article  Google Scholar 

  3. Beyene, W.: Application of artificial neural networks to statistical analysis and nonlinear modeling of high-speed interconnect systems. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 26, 166–176 (2007)

    Article  Google Scholar 

  4. Ambasana, N., Gope, D., Mutnury, B., Anand, G.: Eye-height/width prediction from S-parameters using bounded size training set for ANN. In: 2014 IEEE Electrical Design of Advanced Packaging and Systems Symposium (EDAPS) (2014)

    Google Scholar 

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

    Article  Google Scholar 

  6. Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4, 251–257 (1991)

    Article  Google Scholar 

  7. Devabhaktuni, V., Zhang, Q.: Neural network training-driven adaptive sampling algorithm for microwave modeling. In: 30th European Microwave Conference (2000)

    Google Scholar 

  8. Hagan, M., Menhaj, M.: Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5, 989–993 (1994)

    Article  Google Scholar 

  9. Haykin, S.: Neural Networks and Learning Machines. Prentice Hall, Upper Saddle River (2009)

    Google Scholar 

  10. Koziel, S., Yang, X., Zhang, Q.: Simulation-Driven Design Optimization and Modeling for Microwave Engineering. World Scientific Publishing Company, Singapore (2013)

    Book  Google Scholar 

  11. Devabhaktuni, V., Yagoub, M., Zhang, Q.: A robust algorithm for automatic development of neural-network models for microwave applications. IEEE Trans. Microw. Theory Tech. 49, 2282–2291 (2001)

    Article  Google Scholar 

  12. Rayas-sanchez, J., Gutierrez-Ayala, V.: EM-based statistical analysis and yield estimation using linear-input and neural-output space mapping. In: 2006 IEEE MTT-S International Microwave Symposium Digest (2006)

    Google Scholar 

  13. Eason, J., Cremaschi, S.: Adaptive sequential sampling for surrogate model generation with artificial neural networks. Comput. Chem. Eng. 68, 220–232 (2014)

    Article  Google Scholar 

  14. RF Toolbox - MATLAB.

  15. Neural Network Toolbox - MATLAB.

Download references


This work is supported by the Universiti Sains Malaysia under the Research University Grant (RUI) grant no. 1001/PELECT/8014011.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Patrick Goh .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Goay, C.H., Goh, P. (2017). Neural Networks for Eye Height and Eye Width Prediction with an Improved Adaptive Sampling Algorithm. In: Mohamed Ali, M., Wahid, H., Mohd Subha, N., Sahlan, S., Md. Yunus, M., Wahap, A. (eds) Modeling, Design and Simulation of Systems. AsiaSim 2017. Communications in Computer and Information Science, vol 751. Springer, Singapore.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6462-3

  • Online ISBN: 978-981-10-6463-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics