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

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

研究成果: Article

2 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)2612-2621
頁數10
期刊Expert Systems With Applications
34
發行號4
DOIs
出版狀態Published - 2008 五月 1

All Science Journal Classification (ASJC) codes

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

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