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Improved learning in grid-to-grid neural network via clustering

Conference Paper


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Abstract


  • The maze traversal problem involves finding the shortest distance to the goal from any position in a maze. Such maze solving problems have been an interesting challenge in computational intelligence. Previous work has shown that grid-to-grid neural networks such as the cellular simultaneous recurrent neural network (CSRN) can effectively solve simple maze traversing problems better than other iterative algorithms such as the feedforward multi layer perceptron (MLP). In this work, we investigate improved learning for the CSRN maze solving problem by exploiting relevant information about the

    maze. We cluster parts of the maze using relevant state information and show an improvement in learning performance. We also study the effect of the number of clusters on the learning rate for the maze solving problem. Furthermore, we investigate a few code optimization techniques

    to improve the run time efficiency. The outcome of this research may have direct implication in rapid search and recovery, disaster planning and autonomous navigation among others.

Authors


  •   White, William E. (external author)
  •   Iftekharuddin, Khan M. (external author)
  •   Bouzerdoum, Salim

Publication Date


  • 2010

Citation


  • White, W. E., Iftekharuddin, K. M. & Bouzerdoum, A. (2010). Improved learning in grid-to-grid neural network via clustering. IEEE World Congress on Computational Intelligence (pp. 2318-2324). USA: IEEE.

Scopus Eid


  • 2-s2.0-79959440039

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1839&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/826

Has Global Citation Frequency


Start Page


  • 2318

End Page


  • 2324

Place Of Publication


  • USA

Abstract


  • The maze traversal problem involves finding the shortest distance to the goal from any position in a maze. Such maze solving problems have been an interesting challenge in computational intelligence. Previous work has shown that grid-to-grid neural networks such as the cellular simultaneous recurrent neural network (CSRN) can effectively solve simple maze traversing problems better than other iterative algorithms such as the feedforward multi layer perceptron (MLP). In this work, we investigate improved learning for the CSRN maze solving problem by exploiting relevant information about the

    maze. We cluster parts of the maze using relevant state information and show an improvement in learning performance. We also study the effect of the number of clusters on the learning rate for the maze solving problem. Furthermore, we investigate a few code optimization techniques

    to improve the run time efficiency. The outcome of this research may have direct implication in rapid search and recovery, disaster planning and autonomous navigation among others.

Authors


  •   White, William E. (external author)
  •   Iftekharuddin, Khan M. (external author)
  •   Bouzerdoum, Salim

Publication Date


  • 2010

Citation


  • White, W. E., Iftekharuddin, K. M. & Bouzerdoum, A. (2010). Improved learning in grid-to-grid neural network via clustering. IEEE World Congress on Computational Intelligence (pp. 2318-2324). USA: IEEE.

Scopus Eid


  • 2-s2.0-79959440039

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1839&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/826

Has Global Citation Frequency


Start Page


  • 2318

End Page


  • 2324

Place Of Publication


  • USA