Self-structuring hidden control neural models

Abstract

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.<>