Nonlinear Black-box Modeling in System Identiication

Regressor Selection and Wavelet Network Construction

The wavelet network 22, 23] has been introduced as a special feedforward neural network supported by the wavelet theory. Such network can be directly used in function approximation problems, and consequently can be applied to nonlinear system modeling by means of nonlinear black-box identiication. In this paper the construction of feedforward neural networks is discussed from both identiication...

Identiication of Multivariable Hammerstein Systems Using Rational Orthonormal Bases

In this paper, a non iterative algorithm for the simultaneous identiication of the linear and nonlinear parts of multivariable Hammerstein systems is presented. The proposed algorithm is numerically robust, since it is based only on least squares estimation and singular value decomposition. Under weak assumptions on the persistency of excitation of the inputs, the algorithm provides consistent ...

Ensuring Certain Physical Properties in Black Box Models by Applying Fuzzy Techniques

We consider the situation where a nonlinear physical system is identiied from input-output data. In case no speciic physical structural knowledge about the system is available, parameterized grey box models cannot be used. Identiication in black-box-type of model structures is then the only alternative, and general approaches like neural nets, neuro-fuzzy models, etc., have to be applied. Howev...

Some Aspects on Nonlinear Black - Box Modeling in System Identi

A Comparison Between Semi-Physical and Black-Box Neural Net Modeling: A Case Study

This paper considers identiication of a solar-heated house. Using prior physical knowledge and a semi-physical modeling procedure, a set of physically motivated regressors are determined. With these as inputs a reasonable neural network model of the plant is estimated.