This article presents numerical studies on semi-active seismic response control of structures equipped with Magneto-Rheological (MR) dampers. A multi-layer artificial neural network (ANN) was employed to mitigate the influence of time delay, This ANN was trained using data from the El-Centro earthquake. The inputs of ANN are the seismic responses of the structure in the current step, and the ou...

This paper presents a feed forward back-propagation neural network model to predict the retained tensile strength and design chart in order to estimation of the strength reduction factors of nonwoven geotextiles due to installation process. A database of 34 full-scale field tests were utilized to train, validate and test the developed neural network and regression model. The results show that t...

Anonlinearadaptive  ight control system is proposed using a backstepping and neural networks controller. The backstepping controller is used to stabilize all state variables simultaneouslywithout the two-timescale assumption that separates the fast dynamics, involvingthe angularrates of the aircraft, from the slow dynamics,which includes angle of attack, sideslip angle, and bank angle. It is a...

The basic robot control technique is the model based computed-torque control which is known to suuer performance degradation due to model uncertainties. Adding a neural network (NN) controller in the control system is one eeective way to compensate for the ill eeects of these uncertainties. In this paper a systematic study of NN controller for a robot manipulator under a uniied computed-torque ...

estimating the final price of products is of great importance. for manufacturing companies proposing a final price is only possible after the design process over. these companies propose an approximate initial price of the required products to the customers for which some of time and money is required. here using the existing data of already designed transformers and utilizing the bayesian anal...

A dynamics inversion compensation scheme is designed for control of nonlinear discretetime systems with input backlash. The compensator uses the dynamic inversion technique, with a neural network (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamics preinverse of an invertible discrete time dynamic...

this paper is concerned with the problem of delay dependent H∞ control of discrete-time uncertain recurrent neural networks with time varying-delays. The neural network is subject to parameter uncertainty, and time-varying delay. For the robust H∞ stabilization problem, a state feedback controller is designed to ensure global robust stability of the closed-loop system about its equilibrium poin...