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Journal ArticleDOI

Active Control of Structures Using Neural Networks

Jamshid Ghaboussi, +1 more
- 01 Apr 1995 - 
- Vol. 121, Iss: 4, pp 555-567
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TLDR
In this paper, a neural-network-based control algorithm has been developed and tested in the computer simulation of active control of a three-story frame structure subjected to ground excitations.
Abstract
A new neural-network-based control algorithm has been developed and tested in the computer simulation of active control of a three-story frame structure subjected to ground excitations. First, an emulator neural network has been trained to forecast the future response of the structure from the immediate history of the system's response, which consists of the structure plus an actuator. The trained emulator has been used in predicting the future responses and in evaluating the sensitivities of the control signal with respect to those responses. At each time step of the simulation, the control signal has been adjusted to induce the required control force in the actuator based in a control criterion. A controller neural network has been trained to learn the relation between the immediate history of response of the structure and actuator, and the adjusted control signals. The trained neurocontroller has been used in controlling the structure for different dynamic loading conditions. Results of this initial st...

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Citations
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References
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Book

Introduction To The Theory Of Neural Computation

TL;DR: This book is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning.
Journal ArticleDOI

Approximation capabilities of multilayer feedforward networks

TL;DR: It is shown that standard multilayer feedforward networks with as few as a single hidden layer and arbitrary bounded and nonconstant activation function are universal approximators with respect to L p (μ) performance criteria, for arbitrary finite input environment measures μ.
Proceedings Article

The Cascade-Correlation Learning Architecture

TL;DR: The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network.
Book

Neural networks for control

TL;DR: This work addresses general issues of neural network based control and neural network learning with regard to specific problems of motion planning and control in robotics, and takes up application domains well suited to the capabilities of Neural network controllers.
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