Volume 3, Number 1 (3-2013)                   2013, 3(1): 179-207 | Back to browse issues page


XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Ghodrati Amiri G, Namiranian P. HYBRID ARTIFICIAL NEURAL NETWORKS BASED ON ACO-RPROP FOR GENERATING MULTIPLE SPECTRUM-COMPATIBLE ARTIFICIAL EARTHQUAKE RECORDS FOR SPECIFIED SITE GEOLOGY. International Journal of Optimization in Civil Engineering. 2013; 3 (1) :179-207
URL: http://ijoce.iust.ac.ir/article-1-126-en.html

Abstract:   (3378 Views)
The main objective of this paper is to use ant optimized neural networks to generate artificial earthquake records. In this regard, training accelerograms selected according to the site geology of recorder station and Wavelet Packet Transform (WPT) used to decompose these records. Then Artificial Neural Networks (ANN) optimized with Ant Colony Optimization and resilient Backpropagation algorithm and learn to relate the dimension reduced response spectrum of records to their wavelet packet coefficients. Trained ANNs are capable to produce wavelet packet coefficients for a specified spectrum, so by using inverse WPT artificial accelerograms obtained. By using these tools, the learning time of ANNs reduced salient and generated accelerograms had more spectrum-compatibility and save their essence as earthquake accelerograms.
Full-Text [PDF 1311 kb]   (1905 Downloads)    
Type of Study: Research | Subject: Optimal design
Received: 2013/01/24

Send email to the article author


© 2015 All Rights Reserved | Iran University of Science & Technology

Designed & Developed by : Yektaweb