  This paper proposes a probabilistic neural network developed on the basis of
time-series discriminant component analysis (TSDCA) that can be used to
classify high-dimensional time-series patterns. TSDCA involves the compression
of high-dimensional time series into a lower-dimensional space using a set of
orthogonal transformations and the calculation of posterior probabilities based
on a continuous-density hidden Markov model with a Gaussian mixture model
expressed in the reduced-dimensional space. The analysis can be incorporated
into a neural network, which is named a time-series discriminant component
network (TSDCN), so that parameters of dimensionality reduction and
classification can be obtained simultaneously as network coefficients according
to a backpropagation through time-based learning algorithm with the Lagrange
multiplier method. The TSDCN is considered to enable high-accuracy
classification of high-dimensional time-series patterns and to reduce the
computation time taken for network training. The validity of the TSDCN is
demonstrated for high-dimensional artificial data and EEG signals in the
experiments conducted during the study.
