NEURAL NETWORK ALGORITHMS BASED
METHODS FOR ADAPTIVE CONTROL OF COMPLEX
OBJECTS BELONGING TO THE CLASS
OF NONLINEAR DYNAMICAL SYSTEMS
G. F. Malychina, A. V. Merkusheva
Applied elements of the complex
object control theory are considered when the object is characterized by
many state parameters, nonlinear dynamics equations for parameters and the
measurement system. The presen-tation, identification and control of such
object (as a nonlinear multi-parametric dynamic system, DS) based on
the neural networks methodology are and described compared with those for
linear DS. For the case of incom-plete information concerning the object
model, the use of an adaptive control scheme is demonstrated.