Plant (subsystem) state control at incomplete measurement information on the parameter set determining its dynamics. I. Structure analysis of a neural network adapted to the analyzed information dynamics

The limitedness of the signal local-stationarity concept in information measurement and information control systems (ICS) reflecting the state of the controlled plant (subsystem) demands the use of more perfect analysis and processing methods, time-frequency transformations and algorithms for neural networks (NN). Significant problems occur when the plant (subsystem) state control is implemented in conditions where some parameters have no effect on the measuring system sensors, i.e. in conditions of incomplete information. The solution of this problem is obtained based on the analysis of plant-ICS system dynamics equations (in the state parameter space) and on the use of temporal NN algorithms. The first (of three) paper parts discusses the NN structure and learning algorithms that may adequately represent the data and controlled process dynamics. NN structures are analyzed on the basis of a neural filter concept and learning - on the basis of a time-dependent back-propagation algorithm.