Predictive Modular Neural Networks for Time Series Classification.
A predictive modular neural network (PREMONN) architecture for time series classification is presented. The PREMONN has a hierarchical structure. The bottom level consists of a bank of linear or nonlinear predictor modules. The top level is a decision module which employs Bayesian or nonprobabilistic decision rules. For various choices of prediction and decision modules, convergence to correct classification is proven. Also it is shown that PREMONN is robust to noise and the speed/accuracy tradeoff is investigated. The analysis is mainly mathematical; however, we also present classification experiments to corroborate our conclusions. Copyright 1996 Elsevier Science Ltd.
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