Abstract
We present in this paper the implementation of the data driven method we called DTS (Divide to Simplify), that builds dynamically a Multi-Neural Network Architecture. The Multi-Neural Network architecture, we propose, solves a complex problem by splitting it into several easier problems. We have previously present a software version of the DTS multi-neural network architecture. The main idea of the DTS approach is to use a set of small and specialized mapping neural networks, or Slave Neural Networks (SNN), that are guided by a prototype based neural network, or Master Neural Network (MNN). In this paper, the MNN manages a set of hardware digital neural networks. Learning is performed in few milliseconds. We get a very good rate of classification when using the two spirals problem as a benchmark.
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© 1997 Springer-Verlag Berlin Heidelberg
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Chebira, A., Madani, K., Mercier, G. (1997). Multi-Neural Networks hardware and software architecture: Application of the divide to simplify paradigm DTS. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032544
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DOI: https://doi.org/10.1007/BFb0032544
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