Title: Active control of building structure using lattice probabilistic neural network based on learning algorithm

Authors: Seongkyu Chang; Dookie Kim

Addresses: Department of Civil and Environmental Engineering, Kunsan National University, Kunsan, Jeonbuk 573-701, Korea. ' Department of Civil and Environmental Engineering, Kunsan National University, Kunsan, Jeonbuk 573-701, Korea

Abstract: Active control of building structure using lattice probabilistic neural network (LPNN) employing the gradient descent method (GDM) for learning to increase control capability is proposed. With the lattice pattern of the state vector used as the training data, LPNN calculates the control force using only the adjacent information of input, thus, response is greatly faster. Three story building under El Centro earthquake is used to train the LPNN. Northridge earthquake is used to verify the proposed method. In the numerical simulation of the building structure control, the control results of the LPNN are compared with the uncontrolled results. The proposed LPNN algorithm can effectively reduce the response of the building structure under earthquakes.

Keywords: lattice probabilistic neural networks; LPNN; learning algorithms; active control; earthquakes; building structures; structural engineering; gradient descent method; three story buildings; seismic demands.

DOI: 10.1504/IJSTRUCTE.2012.045045

International Journal of Structural Engineering, 2012 Vol.3 No.1/2, pp.75 - 82

Published online: 20 Aug 2014 *

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