PLANT  (SUBSYSTEM)  STATE  CONTROL
AT  INCOMPLETE  MEASUREMENT  INFORMATION
ON  THE  PARAMETER  SET  DETERMINING  ITS  DYNAMICS.
III. RECURSIONS  FOR  PLANT  DYNAMICS  AND  PLANT 
PARAMETERS MEASUREMENT  USING  NEURAL  NETWORKS

                                        Saint-Petersburg

    A sufficiently great number of measurement problems arises from the necessity to estimate multidimensional (vector) characteristic of the controlled plant state and, as a matter of fact, relates to the problems of measuring dynamic system parameters. The current state of a dynamic system (DS) is represented by a set of parameters, the value of which depends on the system whole preceding behavior, and determines the evolution of the future behavior. Control of such object by means of measurement systems (MS) is essentially complicated by the fact that lack some state parameters do not influence measurement sensors, i.e. by incomplete measurement infor-mation. The problem is solved by transforming the equations describing the object dynamics and MS into recur-sive form. The use of neural network (NN), algorithms with adaptive reaction to changing input data, allows one to simulate (without direct digital methods) the dynamics of object-state parameters that does not influence MS sensors.