Thomas K. Caughey

TL;DR: In this article, a neural network-based approach is presented for the detection of changes in the characteristics of structure-unknown systems, which relies on the use of vibration measurements from a 'healthy' system to train the neural network for identification purposes.

TL;DR: In this article, a time-domain procedure for the identification of nonlinear vibrating structures, presented in a companion paper, is applied to a "calibration" problem which incorporates realistic test situations and nonlinear structural characteristics widely encountered in the applied mechanics field.

TL;DR: In this article, the response of a nonlinear string to random excitation is investigated and it is shown that, owing to the additional stress induced by the stretching of the string, the mean squared deflection at every point is smaller than that for the equivalent linear string.

TL;DR: This article applies discrete dynamical systems theory to the dynamic analysis of a simplified vertical hopping robot model that is analogous to Raibert's hopping machines, and finds that the strange attractor can be controlled and eliminated by tuning an appropriate parameter corresponding to the duration of applied hop ping thrust.

TL;DR: The analogy of the neural network procedure to a qualitatively similar non-parametric identification approach, which was previously developed by the authors for handling arbitrary non-linear systems, is discussed and the utility of the Neural network approach is demonstrated by application to several illustrative problems.