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Neural network training with global optimization techniques.

Akio Yamazaki1, Teresa B Ludermir

  • 1Center of Informatics, Federal University of Pernambuco, Cidade Universitária, P.O. Box 7851, Recife, Pernambuco, 50.732-970, Brazil. ay@cin.ufpe.br

International Journal of Neural Systems
|August 19, 2003
PubMed
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This study optimized neural network design for artificial nose odor recognition using Simulated Annealing and Tabu Search. These methods efficiently created accurate, compact neural networks for improved performance.

Area of Science:

  • Computational intelligence
  • Artificial intelligence
  • Machine learning

Background:

  • Artificial noses require efficient neural networks for accurate odor recognition.
  • Optimizing both network architecture and weights simultaneously is a complex challenge.

Purpose of the Study:

  • To investigate the use of metaheuristic algorithms for simultaneous neural network optimization.
  • To apply these methods to the specific problem of odor recognition in artificial noses.

Main Methods:

  • Simulated Annealing (SA) and Tabu Search (TS) were employed for joint architecture and weight optimization.
  • The backpropagation algorithm was used for fine-tuning the neural networks.

Main Results:

  • Both SA and TS yielded neural networks with high classification accuracy and reduced complexity.

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  • The optimized networks demonstrated improved generalization capabilities.

    • Conclusions:

    • Metaheuristic search methods like SA and TS are effective for designing compact and efficient neural networks.
    • This approach is well-suited for odor recognition tasks in artificial nose applications.