This paper discusses a neural-network controller used to reduce the seismic
response of building structures. The training process is carried out throu
gh a novel gradient-search technique that minimizes a desired cost function
defined in terms of a set of structural response quantities. The proposed
training approach does not require an additional neural network for the ide
ntification of the structural system. The training method is quite flexible
with regard to the choice of the cost function to be minimized. A series o
f earthquake, base-acceleration time histories are considered to train the
network. The effectiveness of the controller is demonstrated through severa
l sets of numerical results obtained for a multistory building equipped wit
h an active tuned mass damper.