Neural networks for inverse problems using principal component analysis and orthogonal arrays

被引：0
|
作者：
Kim, Yong Y. [1 ,2 ,3 ]
Kapania, Rakesh K. [1 ,2 ,4 ,5 ]
机构：
[1] Virginia Polytechnic Institute and State University, Blacksburg, VA 24061
[2] Department of Aerospace and Ocean Engineering
[3] Center for Healthcare Technology Development, Chonbuk National University, Jeonjusi, Jeonbuk 561-756, Korea, Republic of
[4] Multidisciplinary Analysis and Design Center for Advanced Vehicles
[5] AIAA
来源：
AIAA Journal | 2006年 / 44卷 / 07期
关键词：
An obstacle in applying artificial neural networks (NNs) to system identification problems is that the dimension and the size of the training set for NNs can be too large to use them effectively in solving a problem with available computational resources. To overcome this obstacle; principal component analysis (PCA) can be used to reduce the dimension of the inputs for the NNs without impairing the integrity of data and orthogonal arrays (OAs) can be used to select a smaller number of training sets that can efficiently represent the given behavior system. NNs with PCA and OAs are used here in solving two parameter identification problems in two different fields. The first problem is identifying the location of damage in cantilever plates using the free vibration response of the structure. The free vibration response is simulated using the finite element method. The second problem is identifying an anomaly in an illuminated opaque homogeneous tissue using near-infrared light based on the simulation of the photon intensity and the photon mean time of flight in perfect and imperfect tissues using the finite element method;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Journal article (JA)
引用
收藏
页码：1628 / 1634
相关论文
共 50 条
  • [21] Principal component analysis using neural network
    Yang, Jian-Gang
    Sun, Bin-Qiang
    Journal of Zhejinag University: Science, 2002, 3 (03): : 298 - 304
  • [22] Principal component analysis using neural network
    Jian-gang Yang
    Bin-qiang Sun
    Journal of Zhejiang University-SCIENCE A, 2002, 3 (3): : 298 - 304
  • [23] Blind source separation using principal component neural networks
    Diamantaras, KI
    ARTIFICIAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS, 2001, 2130 : 515 - 520
  • [24] Viscosity of carbon nanotube suspension using artificial neural networks with principal component analysis
    Yousefi, Fakhri
    Karimi, Hajir
    Mohammadiyan, Somayeh
    HEAT AND MASS TRANSFER, 2016, 52 (11) : 2345 - 2355
  • [25] Local linear neural networks based on principal component analysis
    Ramfath, L.
    Muenchof, M.
    Isermann, R.
    2006 AMERICAN CONTROL CONFERENCE, VOLS 1-12, 2006, 1-12 : 3050 - +
  • [26] Nonlinear principal component analysis for the radiometric inversion of atmospheric profiles by using neural networks
    Del Frate, F
    Schiavon, G
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1999, 37 (05): : 2335 - 2342
  • [27] Viscosity of carbon nanotube suspension using artificial neural networks with principal component analysis
    Fakhri Yousefi
    Hajir Karimi
    Somayeh Mohammadiyan
    Heat and Mass Transfer, 2016, 52 : 2345 - 2355
  • [28] Protein Superfamily Classification Using Kernel Principal Component Analysis And Probabilistic Neural Networks
    Vipsita, Swati
    Shee, Bithin Kanti
    Rath, Santanu Kumar
    2011 ANNUAL IEEE INDIA CONFERENCE (INDICON-2011): ENGINEERING SUSTAINABLE SOLUTIONS, 2011,
  • [29] On the neural networks of empathy: A principal component analysis of an fMRI study
    Jason S Nomi
    Dag Scherfeld
    Skara Friederichs
    Ralf Schäfer
    Matthias Franz
    Hans-Jörg Wittsack
    Nina P Azari
    John Missimer
    Rüdiger J Seitz
    Behavioral and Brain Functions, 4
  • [30] Using Principal Component Analysis and Autoassociative Neural Networks to Estimate Missing Data in a Database
    Mistry, Jaisheel
    Nelwamondo, Fulufhelo V.
    Marwala, Tshilidzi
    WMSCI 2008: 12TH WORLD MULTI-CONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL V, PROCEEDINGS, 2008, : 24 - 29
12345