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  • 标题:Time Series Modeling Using Artificial Neural Networks
  • 本地全文:下载
  • 作者:P.Ram Kumar ; M.V.Ramana Murthy ; D.Eashwar
  • 期刊名称:Journal of Theoretical and Applied Information Technology
  • 印刷版ISSN:1992-8645
  • 电子版ISSN:1817-3195
  • 出版年度:2008
  • 卷号:4
  • 期号:12
  • 出版社:Journal of Theoretical and Applied
  • 摘要:

    An Artificial Intelligent technique exists in human brain for observation Of behavior of neurons in human brain. A neural network achieves the intelligent results by using massively parallel computations rather than by using rules or other logical structures. A set of elements begins by being randomly connected. Then network is trained to recognize a pattern by strengthening signals that lead to appropriate results and weakening incorrect or inefficient signals. An Artificial Neural Network(ANN) has procedural rules or formulas for only what kinds of input data the neural network can use to make an association with desired output. The approach of ANN has several advantages over conventional statistical and
    deterministic` approaches. One of the most important algorithms of ANN is “Back Propogation Algorithm”(BPA) which learns by computing an error signal and than propogating the error backword throw the network. The BPA method is applied to statistical model(ARIMA) to test its efficiency and then applied to some actual geophysical data. The result of the analysis shows that the ANN is fast and efficient method for simulating/modeling large amount of data.

  • 关键词:Time Series Modeling;Artificial Neural Networks;Logical Structures;Geophysical Data;Neural Network
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