Modeling of direction-dependent processes using Wiener models and neural networks with nonlinear output error structure
UNSPECIFIED. (2004) Modeling of direction-dependent processes using Wiener models and neural networks with nonlinear output error structure. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 53 (3). pp. 744-753. ISSN 0018-9456Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/TIM.2004.827083
The modeling of direction-dependent dynamic processes using Wiener models and recurrent neural network models with nonlinear output error structure is considered. The results obtained are compared for several simulated first-order and second-order processes and using three different types of input signals: a pseudorandom binary signal, an inverse-repeat pseudorandom binary signal and a multisine (sum of harmonics) signal. Experimental results on a real system, namely an electronic nose system, are also presented to illustrate the applicability of the techniques discussed.
|Item Type:||Journal Article|
|Subjects:||T Technology > TK Electrical engineering. Electronics Nuclear engineering|
|Journal or Publication Title:||IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT|
|Publisher:||IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC|
|Number of Pages:||10|
|Page Range:||pp. 744-753|
Actions (login required)