Paper Title:
A Predictive Dynamic Neural Network Model Based on Principal Component Analysis (PCA) and its Application
  Abstract

We propose a real-time prediction method based on PCA and improved multi-step Elman net. The method not only preserves most original data information, but also eliminates the relativity among data and simplifies the net structure. It can predict the complex and nonlinear systems with dynamic recurrent algorithm. Through training the net has the ability of adapting to the uncertainty of the nonlinear structure, and then reflects the dynamic character of the systems. The hit rate reaches 88.17% to forecast the silicon content in hot metal of a blast furnace with errors ranging from-0.05 percent to 0.05 percent. The results prove that this method is feasible to predict silicon content on blast furnace and also it’s a prediction method of nice future.

  Info
Periodical
Chapter
Chapter 1: Numbers, Intelligence
Edited by
Quanjie Gao
Pages
19-24
DOI
10.4028/www.scientific.net/AMM.127.19
Citation
Q. Y. Yan, Y. Q. Liu, "A Predictive Dynamic Neural Network Model Based on Principal Component Analysis (PCA) and its Application", Applied Mechanics and Materials, Vol. 127, pp. 19-24, 2012
Online since
October 2011
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Price
$35.00
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