دوره 3، شماره 1 - ( 12-1391 )                   جلد 3 شماره 1 صفحات 179-207 | برگشت به فهرست نسخه ها


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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-fa.html
HYBRID ARTIFICIAL NEURAL NETWORKS BASED ON ACO-RPROP FOR GENERATING MULTIPLE SPECTRUM-COMPATIBLE ARTIFICIAL EARTHQUAKE RECORDS FOR SPECIFIED SITE GEOLOGY. دانشگاه علم و صنعت ایران. 1391; 3 (1) :179-207

URL: http://ijoce.iust.ac.ir/article-1-126-fa.html


چکیده:   (3369 مشاهده)
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.
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نوع مطالعه: پژوهشي | موضوع مقاله: Optimal design
دریافت: ۱۳۹۱/۱۱/۵

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