Towards wiener system identification with minimum a-priori information

The ability to construct accurate mathematical models of real systems is an important part of control systems design. A block oriented systems identification approach models the unknown system as interconnected linear and nonlinear blocks. The subject of this thesis is a particular configuration of these blocks referred to as a Wiener model. The Wiener model studied here is a cascade of an input linear block followed by a nonlinear block which then provides one output. We assume that the intermediate signal between the linear and nonlinear block is unknown, only the Wiener model input and output can be sampled.;The difficulty of Wiener model identification is the interaction of the linear and nonlinear blocks. If one of the blocks is known then the intermediate signal can be produced and identification becomes much easier. Thus this thesis focuses on identification of the linear transfer function in a Wiener model. The question examined throughout the thesis is: given some "small" amount of a priori information on the nonlinear part, what can we determine about the linear part? Examples of minimal a priori information are knowledge of only one point on the nonlinear characteristic, or the sign of the output or simply that the transfer characteristic is monotonic over a certain range. Nonlinear blocks with and without memory are discussed.;The contributions of this thesis are several algorithms for identifying the linear transfer function of a block oriented Wiener system. These are presented and analyzed in detail. These methods can be applied to either finite or infinite impulse response (i.e. FIR or IIR) linear blocks. Each algorithm has a carefully defined set of a priori information on the nonlinearity. Also, each approach has a set of assumptions on the input excitation.