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 language | English |
|---|---|
| Pages (from-to) | 2612-2621 |
| Number of pages | 10 |
| Journal | Expert Systems With Applications |
| Volume | 34 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2008 May 1 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
- Computer Science Applications
- Artificial Intelligence
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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 journal › Article
TY - JOUR
T1 - Diversity of quantum optimizations for training adaptive support vector regression and its prediction applications
AU - Chang, Bao Rong
AU - Tsai, Hsiu Fen
AU - Young, Chung-Ping
PY - 2008/5/1
Y1 - 2008/5/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=38649096231&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2007.05.001
DO - 10.1016/j.eswa.2007.05.001
M3 - Article
VL - 34
SP - 2612
EP - 2621
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 4
ER -