Tech. Rep. Available

[Marios Polycarpou]

The following paper has been placed in the Neuroprose archives at
Ohio  State.   The  file  is "polycarpou.stability.ps.Z." See ftp in-
structions below.


	IDENTIFICATION AND CONTROL OF NONLINEAR SYSTEMS
   USING NEURAL NETWORK MODELS: DESIGN AND STABILITY ANALYSIS
   
	Marios M. Polycarpou  and  Petros A. Ioannou

	 Department of Electrical Engineering - Systems
           University of Southern California,  MC-2563
	      Los Angeles,  CA  90089-2563,   U.S.A



Abstract:

The feasibility of applying neural network learning techniques 
in problems of system identification and control has been 
demonstrated through several empirical studies. These studies 
are based for the most part on gradient techniques for deriving 
parameter adjustment laws. While such schemes perform well in 
many cases, in general, problems arise in attempting to prove 
stability of the overall system, or convergence of the output 
error to zero. 
This paper presents a stability theory approach to synthesizing 
and analyzing identification and control schemes for nonlinear 
dynamical systems using neural network models. The nonlinearities 
of the dynamical system are assumed to be unknown and are modelled 
by neural network architectures. Multilayer networks with sigmoidal 
activation functions and radial basis function networks are the two 
types of neural network models that are considered. These static 
network architectures are combined with dynamical elements, in the 
form of stable filters, to construct a type of recurrent network 
configuration which is shown to be capable of approximating a large 
class of dynamical systems. Identification schemes based on neural 
network models are developed using two different techniques, namely,
the Lyapunov synthesis approach and the gradient  method. Both 
identification schemes are shown to guarantee stability, even 
in the presence of modelling errors. A novel network architecture, 
referred to as dynamic radial basis function network, is derived 
and shown to be  useful in problems dealing with learning in dynamic 
enviroments. For a class of nonlinear systems, a stable neural 
network based control configuration is presented and analyzed. 



unix> ftp archive.cis.ohio-state.edu
Name: anonymous
Password: neuron
ftp> cd pub/neuroprose
ftp> binary
ftp> get polycarpou.stability.ps.Z
ftp> quit
unix> uncompress polycarpou.stability.ps.Z
unix> lpr polycarpou.stability.ps



Any comments are welcome!

Marios Polycarpou

e-mail: polycarp at bode.usc.edu