000004964 001__ 4964
000004964 005__ 20141119144608.0
000004964 04107 $$aeng
000004964 046__ $$k2002-06-02
000004964 100__ $$aPei, Jin-Song
000004964 24500 $$aANALYSIS OF VOLTERRA/WIENER NEURAL NETWORKS FOR ADAPTIVE IDENTIFICATION OF HYSTERETIC SYSTEMS

000004964 24630 $$n15.$$pProceedings of the 15th ASCE Engineering Mechanics Division Conference
000004964 260__ $$bColumbia University in the City of New York
000004964 506__ $$arestricted
000004964 520__ $$2eng$$aThis study attempts to demystify a powerful neural network approach, Volterra/Wiener Neural Network (VWNN), for modeling nonlinear hysteretic systems and in turn to streamline its architecture to achieve better computational efficiency. Artificial neural networks are often treated as ”black box” modeling tools, in contrast, here the authors examine problem formulation and network architecture to explore the inner workings of this neural network. Based on the understanding of the dynamics of hysteretic systems, some simplifications and modifications are made to the original VWNN in predicting accelerations of hysteretic systems under arbitrary force excitations in an off-line or even in an adaptive (on-line) mode. The VWNN is able to yield a unique set of weights when the values of the controlling design parameters are fixed. One training example is presented to illustrate the application of the VWNN. 

000004964 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000004964 653__ $$aVWNN, nonlinear, hysteretic.

000004964 7112_ $$a15th ASCE Engineering Mechanics Division Conference$$cNew York (US)$$d2002-06-02 / 2002-06-05$$gEM2002
000004964 720__ $$aPei, Jin-Song$$iSmyth, Andrew W.$$iKosmatopoulos, Elias B.$$iMasri, Sami F.
000004964 8560_ $$ffischerc@itam.cas.cz
000004964 8564_ $$s336427$$uhttp://invenio.itam.cas.cz/record/4964/files/625.pdf$$yOriginal version of the author's contribution as presented on CD, .
000004964 962__ $$r4594
000004964 980__ $$aPAPER