GPD training of the state weighting functions in hidden control neural network

This paper proposes a weighted hidden control neural network (WHCNN) which incorporates a state weighting function into the conventional HCNN. The state weighting function is trained by the generalized probabilistic descent (GPD) method. The GPD method allows minimization of the number of recognition errors directly, and thus allows approximation of the minimum-error-rate recognizer. As a result, we can find that state prediction residuals from each state contain rich discrimination information. Experimental results on the Korean digit recognition have shown 25% and about 16.7% reduction in the number of recognition errors for closed and open test, respectively. The derived algorithm may be easily applied to other predictive neural networks.