Diversity of quantum optimizations for training adaptive support vector regression and its prediction applications

Bao Rong Chang, Hsiu Fen Tsai, Chung-Ping Young

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Three kinds of quantum optimizations are introduced in this paper as follows: quantum minimization (QM), neuromorphic quantum-based optimization (NQO), and logarithmic search with quantum existence testing (LSQET). In order to compare their optimization ability for training adaptive support vector regression, the performance evaluation is accomplished in the basis of forecasting the complex time series through two real world experiments. The model used for this complex time series prediction comprises both BPNN-Weighted Grey-C3LSP (BWGC) and nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) that is tuned perfectly by quantum-optimized adaptive support vector regression. Finally, according to the predictive accuracy of time series forecast and the cost of the computational complexity, the concluding remark will be made to illustrate and discuss these quantum optimizations.

Original languageEnglish
Pages (from-to)2612-2621
Number of pages10
JournalExpert Systems With Applications
Volume34
Issue number4
DOIs
Publication statusPublished - 2008 May 1

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Time series
Computational complexity
Testing
Costs
Experiments

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

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Diversity of quantum optimizations for training adaptive support vector regression and its prediction applications. / Chang, Bao Rong; Tsai, Hsiu Fen; Young, Chung-Ping.

In: Expert Systems With Applications, Vol. 34, No. 4, 01.05.2008, p. 2612-2621.

Research output: Contribution to journalArticle

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