Self-structuring hidden control neural models

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The majority of neural models for pattern recognition have fixed architecture during training. A typical consequence is non-optimal and often too large networks. In this paper we propose a Self-structuring Hidden Control (SHC) neural model for pattern recognition, which establishes a near optimal architecture during training. We typically achieve a significant network architecture reduction in terms of the number of hidden Processing Elements (PE). The SHC model combines self-structuring architecture generation with non-linear prediction and hidden Markov modelling. The paper presents a theorem for self-structuring neural models stating that these models are universal approximators and thus relevant for 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 furthermore be applied to continuous speech recognition.
Original languageEnglish
Title of host publicationNeural Networks for Signal Processing II : Proceedings of the 1992 IEEE-SP Workshop
PublisherIEEE Press
Publication date1992
ISBN (Print)0-7803-0557-4
Publication statusPublished - 1992
Externally publishedYes
Event1992 IEEE Workshop on Neural Networks for Signal Processing - Hotel Marielyst, Helsingoer, Denmark
Duration: 31 Aug 19922 Sep 1992


Workshop1992 IEEE Workshop on Neural Networks for Signal Processing
LocationHotel Marielyst
Internet address


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