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Random vibration and statistical linearization

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TLDR
In this paper, a comprehensive account of statistical linearization with related techniques allowing the solution of a very wide variety of practical non-linear random vibration problems is given, and the principal value of these methods is that they are readily generalized to deal with complex mechanical and structural systems and complex types of excitation such as earthquakes.
Abstract
Interest in the study of random vibration problems using the concepts of stochastic process theory has grown rapidly due to the need to design structures and machinery which can operate reliably when subjected to random loads, for example winds and earthquakes. This is the first comprehensive account of statistical linearization - powerful and versatile methods with related techniques allowing the solution of a very wide variety of practical non-linear random vibration problems. The principal value of these methods is that unlike other analytical methods, they are readily generalized to deal with complex mechanical and structural systems and complex types of excitation such as earthquakes.

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Citations
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Proceedings ArticleDOI

QLC-based design of reference tracking controllers for systems with asymmetric saturating actuators

TL;DR: The performance loci of asymmetric saturating actuators are characterized, methods for their calculation and sketching are presented, and the results are utilized for tracking controllers design.
Journal ArticleDOI

Earthquake response spectra estimation of bilinear hysteretic systems using random-vibration theory method

TL;DR: In this paper, a theoretical procedure to estimate spectral displacement of a hysteretic oscillator with bilinear stiffness excited by band-limited excitation is presented, where the stochastic method of ground-motion simulation is combined with the random vibration theory to compute linear and nonlinear structural response.
Journal ArticleDOI

A probabilistic linearization method for non-linear systems subjected to additive and multiplicative excitations

TL;DR: Numerical applications show as, varying the choice of the weighting functions, it is possible to obtain different linearizations, with a variable degree of accuracy, for the random response of non-linear systems subjected to both additive and multiplicative Gaussian white noises.
Journal ArticleDOI

A probabilistic study of the robustness of an adaptive neural estimation method for hysteretic internal forces in nonlinear MDOF systems

TL;DR: In this paper, the effects of the Volterra/Wiener neural network (VWNN) parameters on the robustness and stability of its estimation capabilities were examined. And the results showed that each design parameter within the VWNN was linked to a certain type of performance sensitivity.
Journal ArticleDOI

Information-Theoretic Statistical Linearization

K.R. Chernyshov
- 01 Jan 2016 - 
TL;DR: A statistical linearization problem statement using the information-theoretic criterion under rather general conditions is proposed and a constructive procedure of estimating the coefficients of the weight function of the linearized model is derived.