This paper presents a nonlinear model of a clamped-clamped microbeam actuated by an electrostatic load with stretching and thermoelastic effects. The frequency of free vibration is calculated by discretization based on the Differential Quadrature (DQ) Method. The frequency is a complex value due to the thermoelastic effect that dissipates energy. By separating the real and imaginary parts of fr...

This study presents the effects of project uncertainties on nonlinear time-cost tradeoff (TCT) profile of real life engineering projects by the fusion of fuzzy logic and artificial neural network (ANN) models with hybrid meta-heuristic (HMH) technique, abridged as Fuzzy-ANN-HMH. Nonlinear time-cost relationship of project activities is dealt with ANN models. ANN models are then integrated with ...

it is necessary to use empirical models for estimating of instantaneous peak discharge because of deficit of gauging stations in the country. hence, at present study, two models including artificial neural networks and nonlinear multivariate regression were used to predict peak discharge in taleghan watershed. maximum daily mean discharge and corresponding daily rainfall, one day antecedent and...

Modifications of the non-linear Schr\"odinger model (MNLS) $ i \partial_{t} \psi(x,t) + \partial^2_{x} - [\frac{\delta V}{\delta |\psi|^2} ] = 0,$ where $\psi \in C$ and $V: R_{+} \rightarrow R$, are considered. We show that MNLS models possess infinite towers quasi-conservation laws for soliton-type configurations with a special complex conjugation, shifted parity delayed time reversion (${\ca...

Nonlinear function approximation is one of the most important tasks in system analysis and identification. Several models have been presented to achieve an accurate approximation on nonlinear mathematics functions. However, the majority of the models are specific to certain problems and systems. In this paper, an evolutionary-based wavelet neural network model is proposed for structure definiti...

applying nonlinear models to estimation and forecasting economic models are now becoming more common, thanks to advances in computing technology. artificial neural networks (ann) models, which are nonlinear local optimizer models, have proven successful in forecasting economic variables. most ann models applied in economics use the gradient descent method as their learning algorithm. however, t...