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Self-structuring hidden control neural models

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2 Author(s)
Sorensen, H.B.D. ; Inst. of Electron. Syst., Aalborg Univ., Denmark ; Hartmann, U.

The authors propose a self-structuring hidden control (SHC) neural model for pattern recognition which establishes a near-optimal architecture during training. A significant network architecture reduction in terms of the number of hidden processing elements (PEs) is typically achieved. The SHC model combines self-structuring architecture generation with nonlinear prediction and hidden Markov modelling. The authors present a theorem for self-structuring neural models stating that these models are universal approximators and thus relevant to real-world pattern recognition. Using SHC models containing as few as five hidden PEs each for an isolated word recognition task resulted in a recognition rate of 98.4%. SHC models can also be applied to continuous speech recognition

Published in:

Neural Networks for Signal Processing [1992] II., Proceedings of the 1992 IEEE-SP Workshop

Date of Conference:

31 Aug-2 Sep 1992