research-article

Prediction of ground motion parameters using randomized ANFIS (RANFIS)

Authors:

Pushpak JagtapAuthors Info & Claims

Applied Soft Computing, Volume 40, Issue C

Pages 624 - 634

https://doi.org/10.1016/j.asoc.2015.12.013

Published: 01 March 2016 Publication History

Abstract

Prediction of ground motion parameters using hybrid soft computing technique.The neuro-fuzzy inference system uses Sugeno type fuzzy rules with a randomized fuzzy layer and a linear neural network output layer.Faster prediction of peak ground acceleration, velocity and displacement with increased accuracy. In this paper, a novel neuro-fuzzy learning machine called randomized adaptive neuro-fuzzy inference system (RANFIS) is proposed for predicting the parameters of ground motion associated with seismic signals. This advanced learning machine integrates the explicit knowledge of the fuzzy systems with the learning capabilities of neural networks, as in the case of conventional adaptive neuro-fuzzy inference system (ANFIS). In RANFIS, to accelerate the learning speed without compromising the generalization capability, the fuzzy layer parameters are not tuned. The three time domain ground motion parameters which are predicted by the model are peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The model is developed using the database released by PEER (Pacific Earthquake Engineering Research Center). Each ground motion parameter is related to mainly to four seismic parameters, namely earthquake magnitude, faulting mechanism, source to site distance and average soil shear wave velocity. The experimental results validate the improved performance of the machine, with lesser computation time compared to prior studies.

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Prediction of ground motion parameters using randomized ANFIS (RANFIS)

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Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 40, Issue C

March 2016

683 pages

ISSN:1568-4946

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 March 2016

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Cited By

Wen THe JJiang LDu YJiang L(2024)A simple and flexible bootstrap-based framework to quantify epistemic uncertainty of ground motion models by light gradient boosting machineApplied Soft Computing10.1016/j.asoc.2023.111195152:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.asoc.2023.111195
Teoh Yi Zhe IKeikhosrokiani P(2021)Knowledge workers mental workload prediction using optimised ELANFISApplied Intelligence10.1007/s10489-020-01928-551:4(2406-2430)Online publication date: 1-Apr-2021
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Nguyen HMoayedi HFoong LAl Najjar HJusoh WRashid AJamali J(2020)Optimizing ANN models with PSO for predicting short building seismic responseEngineering with Computers10.1007/s00366-019-00733-036:3(823-837)Online publication date: 1-Jul-2020
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Shihabudheen KMahesh MPillai G(2018)Particle swarm optimization based extreme learning neuro-fuzzy system for regression and classificationExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.09.03792:C(474-484)Online publication date: 1-Feb-2018
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