Identification problems of Recurrent Cascade Neural Network application in predicting an additional mass location
Abstract
The paper is a development of research originated in [8]. The identification problem deals with searching the location of a small mass attached to a steel plate. The corresponding inverse problem is based on measurement of dynamic plate responses on a laboratory model of the plate, taking into account only the bending plate eigenfrequencies. In the inverse analysis the Recurrent Cascade Neural Network was applied, developed in [3]. Much attention is paid to recognition of identification possibilities of RCNN. The testing process is in fact an unsupervised learning, which can lead to unstable and inaccurate recurrence procedure. That is why the verification testing process was carried out adopting the barrier bound approach. These problems are discussed in the present paper.
Keywords
laboratory model of plate, plate eigenfrequencies, Recurrent Cascade Neural Network (RCNN), supervised and unsupervised learning, verification testing, barrier bound,References
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