<?xml version="1.0" encoding="UTF-8"?>
<collection xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd" xmlns="http://www.loc.gov/MARC21/slim">
 <record>
  <leader>00000cab a22000003a 4500</leader>
  <controlfield tag="001">UP-99796217610274143</controlfield>
  <controlfield tag="003">Buklod</controlfield>
  <controlfield tag="005">20231008000647.0</controlfield>
  <controlfield tag="006">a    grb    001 u|</controlfield>
  <controlfield tag="007">ta</controlfield>
  <controlfield tag="008">120424s        xx     d | ||r |||||   ||</controlfield>
  <datafield tag="040" ind1=" " ind2=" ">
   <subfield code="a">DENG</subfield>
  </datafield>
  <datafield tag="041" ind1=" " ind2=" ">
   <subfield code="a">eng</subfield>
  </datafield>
  <datafield tag="100" ind1="0" ind2=" ">
   <subfield code="a">Salajegheh, Eysa</subfield>
  </datafield>
  <datafield tag="245" ind1="0" ind2="0">
   <subfield code="a">Optimum design of structures against earthquake by wavelet neural network and filter banks.</subfield>
  </datafield>
  <datafield tag="300" ind1=" " ind2=" ">
   <subfield code="a">pp. 67-82</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
   <subfield code="a">Optimum design of structures for earthquake is achieved by simulated annealing. To reduce the computational work, a fast wavelet transform is used by means of which the number of points in the earthquake record is decreased. The record is decomposed into two parts. One part contains the low frequency of the recrd, and the other contains the high frequency of the record. The low frequency content is the effective part, since most of the energy of the record is contained in this part of the record. Thus, the low frequency part of the record is used for dynamic analysis. Then, using a wavelet neural network, the dynamic responses of the structures are approximated. By such approximation, the dynamic analysis of the structure becomes unnecessary in the process of optimization. The wavelet neural networks have been employed as a general approximation tool for the time history dynamic analysis. A number of structures are designed for optimal weight and the results are compared to those corresponding to the exact dynamic analysis.</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
   <subfield code="a">Simulated annealing.</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
   <subfield code="a">Fast wavelet transform.</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
   <subfield code="a">Wavelet neural network.</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
   <subfield code="a">Dynamic analysis.</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
   <subfield code="a">Filter banks.</subfield>
  </datafield>
  <datafield tag="773" ind1="0" ind2=" ">
   <subfield code="t">Earthquake engineering &amp; structural dynamics.</subfield>
   <subfield code="g">34, 1 (2005).</subfield>
  </datafield>
  <datafield tag="905" ind1=" " ind2=" ">
   <subfield code="a">FO</subfield>
  </datafield>
  <datafield tag="852" ind1=" " ind2=" ">
   <subfield code="a">UPD</subfield>
   <subfield code="b">DENG</subfield>
  </datafield>
  <datafield tag="942" ind1=" " ind2=" ">
   <subfield code="a">Article</subfield>
  </datafield>
 </record>
</collection>
