Skip to main content

Partitioning for High Performance of Predicting Dynamical Behavior of Color Diffusion in Water using 2-D tightly Coupled Neural Cellular Network

  • Conference paper
Modeling, Simulation and Optimization of Complex Processes

Abstract

The 2-D tightly coupled neural cellular network is developed to simulate the diffusion characteristics of colored liquid dropped onto water surface by generating its own diffusion image. To learn the diffusion characteristics, a large training set was divided into data subsets by extracting the significant feature patterns of the diffusion and simultaneously training the neural cellular network individually. Using this technique for reducing the training-time, increasing the performance, and facilitating the recognition of large data sets, many sub optimal neural networks were developed to replace of one network. Additionally, the result of the partitioning data achieved the speedup of 17.9 times for 12 networks and 605,267 data patterns. The accuracy of the simulated behaviour is more than 90 percent compared with the actual event.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A. Roy, S. Govil and R. Miranda, A Neural-Network Learning Theory and a Polynomial Time RBF Algorithm, IEEE Transactions on Neural Networks, 8, 6 (1997): 1301-1313.

    Article  Google Scholar 

  2. D. Cornforth, and D. Newth, The Kernel Addition Training Algorithm: Faster Training for CMAC Based Neural Networks, Proceedings Conference Artificial Neural Networks and Expert Systems, Otago (2001)

    Google Scholar 

  3. K. Na Nakornphanom, C. Lursinsap, J. Asavanant, and F. C. Lin, Prediction and Animation of Dynamical Behavior of Color Diffusion in Water Using 2-D Tightly Coupled Neural Cellular Network, IEEE International Conference on Systems, Man and Cybernetics (2004)

    Google Scholar 

  4. K. Plaimas, C. Lursinsap, and A. Suratanee, High Performance of Artificial Neural Network of Resolving Ambiguous Nucleotide Problem, 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS-2005), April 2005.

    Google Scholar 

  5. D. E. Rumelhart, and J. L. McCelelland (eds). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press (1987)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Suratanee, A., Nakornphanom, K.N., Plaimas, K., Lursinsap, C. (2008). Partitioning for High Performance of Predicting Dynamical Behavior of Color Diffusion in Water using 2-D tightly Coupled Neural Cellular Network. In: Bock, H.G., Kostina, E., Phu, H.X., Rannacher, R. (eds) Modeling, Simulation and Optimization of Complex Processes. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79409-7_42

Download citation

Publish with us

Policies and ethics